INN Hotels Project

Context

A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.

The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.

The cancellation of bookings impact a hotel on various fronts:

• Loss of resources (revenue) when the hotel cannot resell the room.
• Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
• Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
• Human resources to make arrangements for the guests.

Objective

The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Data Description

The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.

Data Dictionary

• Booking_ID: unique identifier of each booking
• no_of_adults: Number of adults
• no_of_children: Number of Children
• no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
• no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
• type_of_meal_plan: Type of meal plan booked by the customer:
  • Not Selected – No meal plan selected
  • Meal Plan 1 – Breakfast
  • Meal Plan 2 – Half board (breakfast and one other meal)
  • Meal Plan 3 – Full board (breakfast, lunch, and dinner)
• required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
• room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
• lead_time: Number of days between the date of booking and the arrival date
• arrival_year: Year of arrival date
• arrival_month: Month of arrival date
• arrival_date: Date of the month
• market_segment_type: Market segment designation.
• repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
• no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
• no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
• avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
• no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
• booking_status: Flag indicating if the booking was canceled or not.

Importing necessary libraries and data

import pandas as pd
import numpy as np
from sklearn import metrics
import matplotlib.pyplot as plt
%matplotlib inline 
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
# To filter the warnings
import warnings

warnings.filterwarnings("ignore")

# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Library to split data
from sklearn.model_selection import train_test_split


# To build linear model for statistical analysis and prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant

# To get diferent metric scores
from sklearn import metrics
from sklearn.metrics import accuracy_score, roc_curve, confusion_matrix, roc_auc_score
df = pd.read_csv("INNHotelsGroup.csv")

Data Overview

• Observations
• Sanity checks

df.shape

(36275, 19)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB

df.isnull().sum()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64

df.arrival_date.unique()

array([ 2,  6, 28, 20, 11, 13, 15, 26, 18, 30,  5, 10,  4, 25, 22, 21, 19,
       17,  7,  9, 27,  1, 29, 16,  3, 24, 14, 31, 23,  8, 12],
      dtype=int64)

df.nunique()

Booking_ID                              36275
no_of_adults                                5
no_of_children                              6
no_of_weekend_nights                        8
no_of_week_nights                          18
type_of_meal_plan                           4
required_car_parking_space                  2
room_type_reserved                          7
lead_time                                 352
arrival_year                                2
arrival_month                              12
arrival_date                               31
market_segment_type                         5
repeated_guest                              2
no_of_previous_cancellations                9
no_of_previous_bookings_not_canceled       59
avg_price_per_room                       3930
no_of_special_requests                      6
booking_status                              2
dtype: int64

The Booking ID number is essentially a count column. No duplicate values exist to categorize. This column can be excluded for this data processing exercise. The other object columns are type of meal plan, room type reserved, market segment type, and booking status (our dependent variable). These columns each have a low number of set categories that we can use for data processing. These object datatypes will be converted to category datatype below.

data = df.copy()
del data['Booking_ID']
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          36275 non-null  int64  
 1   no_of_children                        36275 non-null  int64  
 2   no_of_weekend_nights                  36275 non-null  int64  
 3   no_of_week_nights                     36275 non-null  int64  
 4   type_of_meal_plan                     36275 non-null  object 
 5   required_car_parking_space            36275 non-null  int64  
 6   room_type_reserved                    36275 non-null  object 
 7   lead_time                             36275 non-null  int64  
 8   arrival_year                          36275 non-null  int64  
 9   arrival_month                         36275 non-null  int64  
 10  arrival_date                          36275 non-null  int64  
 11  market_segment_type                   36275 non-null  object 
 12  repeated_guest                        36275 non-null  int64  
 13  no_of_previous_cancellations          36275 non-null  int64  
 14  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 15  avg_price_per_room                    36275 non-null  float64
 16  no_of_special_requests                36275 non-null  int64  
 17  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(4)
memory usage: 5.0+ MB

data[data.duplicated()].count()

no_of_adults                            10275
no_of_children                          10275
no_of_weekend_nights                    10275
no_of_week_nights                       10275
type_of_meal_plan                       10275
required_car_parking_space              10275
room_type_reserved                      10275
lead_time                               10275
arrival_year                            10275
arrival_month                           10275
arrival_date                            10275
market_segment_type                     10275
repeated_guest                          10275
no_of_previous_cancellations            10275
no_of_previous_bookings_not_canceled    10275
avg_price_per_room                      10275
no_of_special_requests                  10275
booking_status                          10275
dtype: int64

data.drop_duplicates(inplace=True)
df.drop_duplicates(inplace=True)

Dropping duplicate values from the dataset.

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 26000 entries, 0 to 36273
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          26000 non-null  int64  
 1   no_of_children                        26000 non-null  int64  
 2   no_of_weekend_nights                  26000 non-null  int64  
 3   no_of_week_nights                     26000 non-null  int64  
 4   type_of_meal_plan                     26000 non-null  object 
 5   required_car_parking_space            26000 non-null  int64  
 6   room_type_reserved                    26000 non-null  object 
 7   lead_time                             26000 non-null  int64  
 8   arrival_year                          26000 non-null  int64  
 9   arrival_month                         26000 non-null  int64  
 10  arrival_date                          26000 non-null  int64  
 11  market_segment_type                   26000 non-null  object 
 12  repeated_guest                        26000 non-null  int64  
 13  no_of_previous_cancellations          26000 non-null  int64  
 14  no_of_previous_bookings_not_canceled  26000 non-null  int64  
 15  avg_price_per_room                    26000 non-null  float64
 16  no_of_special_requests                26000 non-null  int64  
 17  booking_status                        26000 non-null  object 
dtypes: float64(1), int64(13), object(4)
memory usage: 3.8+ MB

for feature in data.columns: # Loop through all columns in the dataframe
    if data[feature].dtype == 'object': # Only apply for columns with categorical strings
        data[feature] = pd.Categorical(data[feature])# Replace strings with an integer
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 26000 entries, 0 to 36273
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype   
---  ------                                --------------  -----   
 0   no_of_adults                          26000 non-null  int64   
 1   no_of_children                        26000 non-null  int64   
 2   no_of_weekend_nights                  26000 non-null  int64   
 3   no_of_week_nights                     26000 non-null  int64   
 4   type_of_meal_plan                     26000 non-null  category
 5   required_car_parking_space            26000 non-null  int64   
 6   room_type_reserved                    26000 non-null  category
 7   lead_time                             26000 non-null  int64   
 8   arrival_year                          26000 non-null  int64   
 9   arrival_month                         26000 non-null  int64   
 10  arrival_date                          26000 non-null  int64   
 11  market_segment_type                   26000 non-null  category
 12  repeated_guest                        26000 non-null  int64   
 13  no_of_previous_cancellations          26000 non-null  int64   
 14  no_of_previous_bookings_not_canceled  26000 non-null  int64   
 15  avg_price_per_room                    26000 non-null  float64 
 16  no_of_special_requests                26000 non-null  int64   
 17  booking_status                        26000 non-null  category
dtypes: category(4), float64(1), int64(13)
memory usage: 3.1 MB

data["booking_status"].value_counts(1)

Not_Canceled    0.713769
Canceled        0.286231
Name: booking_status, dtype: float64

71% of the data set did not cancel while 29% of the data set did cancel. This ratio will be maintained for modeling.

#data.sort_values(by=['arrival_date', 'arrival_month'], ascending=True)
test = data.loc[(data['arrival_month'] == 2) & (data['arrival_date'] == 29)]

test

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
2626	2	0	1	5	Meal Plan 1	0	Room_Type 1	104	2018	2	29	Online	1	1	0	61.43	0	Canceled
3677	1	0	1	3	Meal Plan 1	0	Room_Type 1	21	2018	2	29	Online	0	0	0	102.05	0	Canceled
5600	2	0	1	3	Meal Plan 1	0	Room_Type 1	24	2018	2	29	Offline	0	0	0	45.50	0	Not_Canceled
6343	1	0	1	1	Meal Plan 1	0	Room_Type 1	117	2018	2	29	Offline	0	0	0	76.00	0	Not_Canceled
7648	2	1	1	5	Meal Plan 1	0	Room_Type 1	35	2018	2	29	Online	0	0	0	98.10	1	Canceled
8000	2	2	1	3	Meal Plan 1	0	Room_Type 6	3	2018	2	29	Online	0	0	0	183.00	1	Not_Canceled
8989	1	0	1	2	Meal Plan 1	0	Room_Type 1	117	2018	2	29	Offline	0	0	0	76.00	0	Not_Canceled
9153	2	2	1	3	Meal Plan 1	0	Room_Type 6	3	2018	2	29	Online	0	0	0	189.75	0	Not_Canceled
9245	2	0	1	3	Meal Plan 1	0	Room_Type 4	15	2018	2	29	Online	0	0	0	85.55	1	Not_Canceled
9664	1	0	1	0	Meal Plan 1	0	Room_Type 4	21	2018	2	29	Online	0	0	0	117.00	0	Not_Canceled
9934	1	0	1	2	Meal Plan 1	0	Room_Type 1	45	2018	2	29	Online	0	0	0	76.30	0	Not_Canceled
10593	2	0	1	3	Meal Plan 1	1	Room_Type 4	47	2018	2	29	Online	0	0	0	99.40	1	Not_Canceled
10652	2	0	1	2	Meal Plan 1	0	Room_Type 1	117	2018	2	29	Offline	0	0	0	86.33	0	Not_Canceled
10747	2	0	1	3	Meal Plan 1	0	Room_Type 1	88	2018	2	29	Online	0	0	0	56.94	0	Canceled
11881	1	0	3	7	Meal Plan 1	0	Room_Type 1	58	2018	2	29	Online	0	0	0	66.45	1	Not_Canceled
13958	1	0	1	1	Meal Plan 1	0	Room_Type 1	0	2018	2	29	Complementary	0	0	0	3.00	0	Not_Canceled
14304	2	0	1	3	Meal Plan 2	0	Room_Type 1	13	2018	2	29	Online	0	0	0	114.55	0	Not_Canceled
15363	1	0	1	2	Meal Plan 1	0	Room_Type 1	61	2018	2	29	Online	0	0	0	78.90	1	Canceled
15438	1	0	1	2	Meal Plan 1	0	Room_Type 1	1	2018	2	29	Offline	0	0	0	76.00	0	Not_Canceled
17202	2	0	1	3	Meal Plan 2	0	Room_Type 1	13	2018	2	29	Online	0	0	0	107.80	0	Not_Canceled
18534	1	0	1	2	Meal Plan 1	0	Room_Type 5	3	2018	2	29	Corporate	0	0	0	107.00	0	Not_Canceled
18680	1	0	1	4	Meal Plan 1	1	Room_Type 1	4	2018	2	29	Corporate	1	0	11	68.00	1	Not_Canceled
19013	1	0	1	1	Meal Plan 1	1	Room_Type 1	7	2018	2	29	Corporate	0	0	0	68.00	0	Not_Canceled
20419	2	0	1	1	Meal Plan 1	0	Room_Type 4	33	2018	2	29	Online	0	0	0	106.40	0	Not_Canceled
21674	1	0	1	1	Meal Plan 1	0	Room_Type 1	117	2018	2	29	Offline	0	0	0	75.00	0	Not_Canceled
21688	2	0	1	2	Meal Plan 1	0	Room_Type 4	57	2018	2	29	Online	0	0	0	95.30	1	Not_Canceled
26108	2	0	1	2	Meal Plan 1	0	Room_Type 1	39	2018	2	29	Online	0	0	0	60.10	0	Not_Canceled
27559	1	0	1	0	Meal Plan 1	0	Room_Type 1	0	2018	2	29	Corporate	1	0	10	65.00	1	Not_Canceled
27928	2	0	1	5	Meal Plan 1	0	Room_Type 4	115	2018	2	29	Online	0	0	0	102.33	1	Not_Canceled
30552	2	1	1	3	Meal Plan 1	0	Room_Type 4	13	2018	2	29	Offline	0	0	0	86.80	0	Canceled
30616	1	0	1	0	Meal Plan 1	0	Room_Type 5	21	2018	2	29	Offline	0	0	0	142.00	0	Not_Canceled
30632	3	0	1	2	Meal Plan 2	0	Room_Type 4	7	2018	2	29	Online	0	0	0	193.00	2	Not_Canceled
32041	2	0	1	0	Not Selected	0	Room_Type 1	50	2018	2	29	Online	0	0	0	76.50	0	Canceled
34638	1	0	1	2	Meal Plan 1	0	Room_Type 1	3	2018	2	29	Corporate	1	0	1	66.00	0	Not_Canceled
35481	1	0	1	1	Meal Plan 1	0	Room_Type 1	7	2018	2	29	Corporate	0	0	0	66.00	0	Not_Canceled

It seems 35 rows of data were entered with the incorred Date/Month/Year of Feb 29, 2018. Since 2018 is not a leap year, this data is not valid. The 35 rows will be deleted from the dataset since the date is not accurate.

test = data[(data['arrival_month'] == 2) & (data['arrival_date'] == 29)].index
data.drop(test, inplace=True)
test = data.loc[(data['arrival_month'] == 2) & (data['arrival_date'] == 29)]
test

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype   
---  ------                                --------------  -----   
 0   no_of_adults                          25965 non-null  int64   
 1   no_of_children                        25965 non-null  int64   
 2   no_of_weekend_nights                  25965 non-null  int64   
 3   no_of_week_nights                     25965 non-null  int64   
 4   type_of_meal_plan                     25965 non-null  category
 5   required_car_parking_space            25965 non-null  int64   
 6   room_type_reserved                    25965 non-null  category
 7   lead_time                             25965 non-null  int64   
 8   arrival_year                          25965 non-null  int64   
 9   arrival_month                         25965 non-null  int64   
 10  arrival_date                          25965 non-null  int64   
 11  market_segment_type                   25965 non-null  category
 12  repeated_guest                        25965 non-null  int64   
 13  no_of_previous_cancellations          25965 non-null  int64   
 14  no_of_previous_bookings_not_canceled  25965 non-null  int64   
 15  avg_price_per_room                    25965 non-null  float64 
 16  no_of_special_requests                25965 non-null  int64   
 17  booking_status                        25965 non-null  category
dtypes: category(4), float64(1), int64(13)
memory usage: 3.1 MB

The 35 rows containing impossible dates were deleted.

pd.to_datetime(dict(year=data.arrival_year, month=data.arrival_month, day=data.arrival_date))
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype   
---  ------                                --------------  -----   
 0   no_of_adults                          25965 non-null  int64   
 1   no_of_children                        25965 non-null  int64   
 2   no_of_weekend_nights                  25965 non-null  int64   
 3   no_of_week_nights                     25965 non-null  int64   
 4   type_of_meal_plan                     25965 non-null  category
 5   required_car_parking_space            25965 non-null  int64   
 6   room_type_reserved                    25965 non-null  category
 7   lead_time                             25965 non-null  int64   
 8   arrival_year                          25965 non-null  int64   
 9   arrival_month                         25965 non-null  int64   
 10  arrival_date                          25965 non-null  int64   
 11  market_segment_type                   25965 non-null  category
 12  repeated_guest                        25965 non-null  int64   
 13  no_of_previous_cancellations          25965 non-null  int64   
 14  no_of_previous_bookings_not_canceled  25965 non-null  int64   
 15  avg_price_per_room                    25965 non-null  float64 
 16  no_of_special_requests                25965 non-null  int64   
 17  booking_status                        25965 non-null  category
dtypes: category(4), float64(1), int64(13)
memory usage: 3.1 MB

data.rename(columns = {'arrival_year':'year', 'arrival_month':'month', 'arrival_date':'day'}, inplace = True)
cols=["year","month","day"]
data['arrival_year_date'] = data[cols].apply(lambda x: '-'.join(x.values.astype(str)), axis="columns")
#pd.to_datetime(dict(year=updatedate.year, month=updatedate.month, day=updatedate.day))
#pd.to_datetime(dict(year=updatedate.year, month=updatedate.month, day=updatedate.day))
data['arrival_year_date'] = data['arrival_year_date'].astype('datetime64[ns]')
data.drop(['year', 'month', 'day'], axis = 1, inplace=True)
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 16 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          25965 non-null  int64         
 1   no_of_children                        25965 non-null  int64         
 2   no_of_weekend_nights                  25965 non-null  int64         
 3   no_of_week_nights                     25965 non-null  int64         
 4   type_of_meal_plan                     25965 non-null  category      
 5   required_car_parking_space            25965 non-null  int64         
 6   room_type_reserved                    25965 non-null  category      
 7   lead_time                             25965 non-null  int64         
 8   market_segment_type                   25965 non-null  category      
 9   repeated_guest                        25965 non-null  int64         
 10  no_of_previous_cancellations          25965 non-null  int64         
 11  no_of_previous_bookings_not_canceled  25965 non-null  int64         
 12  avg_price_per_room                    25965 non-null  float64       
 13  no_of_special_requests                25965 non-null  int64         
 14  booking_status                        25965 non-null  category      
 15  arrival_year_date                     25965 non-null  datetime64[ns]
dtypes: category(4), datetime64[ns](1), float64(1), int64(10)
memory usage: 2.7 MB

Combined the year, month, date columns inot a single arrival_year_date column with a datetime datatype.

data.describe().T

	count	mean	std	min	25%	50%	75%	max
no_of_adults	25965.0	1.890468	0.528516	0.0	2.00	2.0	2.0	4.0
no_of_children	25965.0	0.141190	0.462439	0.0	0.00	0.0	0.0	10.0
no_of_weekend_nights	25965.0	0.882149	0.887862	0.0	0.00	1.0	2.0	7.0
no_of_week_nights	25965.0	2.261814	1.512290	0.0	1.00	2.0	3.0	17.0
required_car_parking_space	25965.0	0.042057	0.200722	0.0	0.00	0.0	0.0	1.0
lead_time	25965.0	66.489313	68.630134	0.0	12.00	44.0	100.0	443.0
repeated_guest	25965.0	0.032659	0.177747	0.0	0.00	0.0	0.0	1.0
no_of_previous_cancellations	25965.0	0.028538	0.409121	0.0	0.00	0.0	0.0	13.0
no_of_previous_bookings_not_canceled	25965.0	0.212555	2.067643	0.0	0.00	0.0	0.0	58.0
avg_price_per_room	25965.0	105.715938	37.871936	0.0	80.75	100.0	127.0	540.0
no_of_special_requests	25965.0	0.742500	0.815293	0.0	0.00	1.0	1.0	5.0

Exploratory Data Analysis (EDA)

• EDA is an important part of any project involving data.
• It is important to investigate and understand the data better before building a model with it.
• A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
• A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Leading Questions:

1. What are the busiest months in the hotel?
2. Which market segment do most of the guests come from?
3. Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?
4. What percentage of bookings are canceled?
5. Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?
6. Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?

# Univariate Analysis
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram
# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot
    
def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()
    
### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()

histogram_boxplot(data,'no_of_adults')

histogram_boxplot(data,'no_of_children')

histogram_boxplot(data,'no_of_weekend_nights')

histogram_boxplot(data,'no_of_week_nights')

histogram_boxplot(data,'lead_time')

histogram_boxplot(data,'no_of_previous_cancellations')

histogram_boxplot(data,'no_of_previous_bookings_not_canceled')

histogram_boxplot(data,'avg_price_per_room')

histogram_boxplot(data,'no_of_special_requests')

labeled_barplot(data, "repeated_guest", perc=True)

Only 3.3% of all guests are repeated guests.

labeled_barplot(data, "required_car_parking_space", perc=True)

Only 4.2% of guests required parking.

labeled_barplot(data, "type_of_meal_plan", perc=True)

labeled_barplot(data, "room_type_reserved", perc=True)

labeled_barplot(data, "market_segment_type", perc=True)

77% of all bookings were made online.

labeled_barplot(data, "booking_status", perc=True)

sns.set(font_scale = 4)
ax = sns.boxplot(x="market_segment_type", y="lead_time", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("Market Segment Type", fontsize = 30)
ax.set_ylabel("Lead Time (days)", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

We can see that offline and online reservations have on average a longer lead time allowing for more time to book another guest in the same room. On average, aviation has the shortest lead time for both booked and cancelled reservations.

sns.set(font_scale = 5)
ax = sns.boxplot(x="market_segment_type", y="avg_price_per_room", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("Market Segment Type", fontsize = 30)
ax.set_ylabel("avg_price_per_room", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

It appears that on average the cancelled rooms are booked at a higher rate than the rooms that were not cancelled across the

sns.set(font_scale = 5)
ax = sns.boxplot(x="market_segment_type", y="no_of_weekend_nights", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("Market Segment Type", fontsize = 30)
ax.set_ylabel("no_of_weekend_nights", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

sns.set(font_scale = 5)
ax = sns.boxplot(x="market_segment_type", y="no_of_week_nights", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("Market Segment Type", fontsize = 30)
ax.set_ylabel("no_of_week_nights", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

sns.set(font_scale = 5)
ax = sns.boxplot(x="no_of_children", y="avg_price_per_room", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("no_of_children", fontsize = 30)
ax.set_ylabel("avg_price_per_room", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

sns.set(font_scale = 5)
ax = sns.boxplot(x="no_of_adults", y="avg_price_per_room", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("no_of_adults", fontsize = 30)
ax.set_ylabel("avg_price_per_room", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

Both number of adults and number of children have generally positive correlations with increasing room price. Children numbers 3 and more show a decrease in cost, while adults show a continuous increase in room price with each additional adult.

stacked_barplot(data, "type_of_meal_plan", "booking_status")

booking_status     Canceled  Not_Canceled    All
type_of_meal_plan                               
All                    7435         18530  25965
Meal Plan 1            5611         14756  20367
Not Selected           1399          3063   4462
Meal Plan 2             424           707   1131
Meal Plan 3               1             4      5
------------------------------------------------------------------------------------------------------------------------

stacked_barplot(data, "market_segment_type", "booking_status")

booking_status       Canceled  Not_Canceled    All
market_segment_type                               
All                      7435         18530  25965
Online                   6795         13204  19999
Offline                   487          3617   4104
Corporate                 130          1276   1406
Aviation                   23            77    100
Complementary               0           356    356
------------------------------------------------------------------------------------------------------------------------

stacked_barplot(data, "room_type_reserved", "booking_status")

booking_status      Canceled  Not_Canceled    All
room_type_reserved                               
All                     7435         18530  25965
Room_Type 1             4942         13675  18617
Room_Type 4             1806          3609   5415
Room_Type 6              393           548    941
Room_Type 2              199           401    600
Room_Type 5               59           171    230
Room_Type 7               34           122    156
Room_Type 3                2             4      6
------------------------------------------------------------------------------------------------------------------------

plt.figure(figsize=(15, 7))
sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, cmap="Spectral")
plt.show()

It appears there may be a slight correlation between number of bookings previously not cancelled and the repeated guest columns. I will wait for confirmation from modeling to see which column to possibly remove.

sns.set(font_scale = 5)
ax = sns.boxplot(x="repeated_guest", y="no_of_previous_bookings_not_canceled", hue = "booking_status", data=data)
ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
ax.set_xlabel("repeated_guest", fontsize = 30)
ax.set_ylabel("no_of_previous_bookings_not_canceled", fontsize = 30)
sns.set(rc = {'figure.figsize':(30,20)})
plt.show()

sns.pairplot(data=data, hue="booking_status" )
#hue="booking_status"
plt.show()

sns.set_style("darkgrid")
data.hist(figsize=(15, 10))
plt.show()

distribution_plot_wrt_target(data, "avg_price_per_room", "booking_status")

distribution_plot_wrt_target(data, "lead_time", "booking_status")

data["booking_status"].value_counts(1)

Not_Canceled    0.713653
Canceled        0.286347
Name: booking_status, dtype: float64

From the data above we see approximately 71% of the rooms booked were not canceled, with 29% of the rooms booked were canceled. We will maintain this ratio of data as we segment for modeling.

data.isnull().values.any()

False

No missing values in the dataset.

Data Preprocessing

• Missing value treatment (if needed)
• Feature engineering (if needed)
• Outlier detection and treatment (if needed)
• Preparing data for modeling
• Any other preprocessing steps (if needed)

EDA

• It is a good idea to explore the data once again after manipulating it.

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 16 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          25965 non-null  int64         
 1   no_of_children                        25965 non-null  int64         
 2   no_of_weekend_nights                  25965 non-null  int64         
 3   no_of_week_nights                     25965 non-null  int64         
 4   type_of_meal_plan                     25965 non-null  category      
 5   required_car_parking_space            25965 non-null  int64         
 6   room_type_reserved                    25965 non-null  category      
 7   lead_time                             25965 non-null  int64         
 8   market_segment_type                   25965 non-null  category      
 9   repeated_guest                        25965 non-null  int64         
 10  no_of_previous_cancellations          25965 non-null  int64         
 11  no_of_previous_bookings_not_canceled  25965 non-null  int64         
 12  avg_price_per_room                    25965 non-null  float64       
 13  no_of_special_requests                25965 non-null  int64         
 14  booking_status                        25965 non-null  category      
 15  arrival_year_date                     25965 non-null  datetime64[ns]
dtypes: category(4), datetime64[ns](1), float64(1), int64(10)
memory usage: 3.7 MB

plt.figure(figsize=(15, 7))
sns.heatmap(pdata.corr(), annot=True, vmin=-1, vmax=1, cmap="Spectral")
plt.show()

There are a few high correlations, that will be closely observed once we begin to trim the model. Number of children and room type 6 reserved have a high correlation of 0.65. Repeated guest also correlates with Corporate market segment with a 0.51 correlation. There are several highly negative correlations between meal plan Type 1 and not selecting a meal plan of -0.87. Room type 1 also has a -0.82 correlation with Room Type 4. The offline market segment has a -0.79 negative correlation with market segment online.

data["date"] = data["arrival_year_date"].values.astype(float)
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 17 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          25965 non-null  int64         
 1   no_of_children                        25965 non-null  int64         
 2   no_of_weekend_nights                  25965 non-null  int64         
 3   no_of_week_nights                     25965 non-null  int64         
 4   type_of_meal_plan                     25965 non-null  category      
 5   required_car_parking_space            25965 non-null  int64         
 6   room_type_reserved                    25965 non-null  category      
 7   lead_time                             25965 non-null  int64         
 8   market_segment_type                   25965 non-null  category      
 9   repeated_guest                        25965 non-null  int64         
 10  no_of_previous_cancellations          25965 non-null  int64         
 11  no_of_previous_bookings_not_canceled  25965 non-null  int64         
 12  avg_price_per_room                    25965 non-null  float64       
 13  no_of_special_requests                25965 non-null  int64         
 14  booking_status                        25965 non-null  category      
 15  arrival_year_date                     25965 non-null  datetime64[ns]
 16  date                                  25965 non-null  float64       
dtypes: category(4), datetime64[ns](1), float64(2), int64(10)
memory usage: 3.9 MB

ldata = data.copy()

numerical_col = ldata.select_dtypes(include=np.number).columns.tolist()
#plt.figure(figsize=(20, 30))
#sns.set(font_scale = 4)
#ax = sns.boxplot(x="market_segment_type", y="lead_time", hue = "booking_status", data=data)
#ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)
#ax.set_xlabel("Market Segment Type", fontsize = 30)
#ax.set_ylabel("Lead Time (days)", fontsize = 30)
#sns.set(rc = {'figure.figsize':(30,20)})
plt.show()


for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)
    plt.rc('axes', titlesize=10) 
    plt.rc('axes', labelsize=10)
    plt.figure(figsize=(10, 10), dpi=80)
plt.show()

<Figure size 800x800 with 0 Axes>

# functions to treat outliers by flooring and capping


def treat_outliers(df, col):
    """
    Treats outliers in a variable

    df: dataframe
    col: dataframe column
    """
    Q1 = df[col].quantile(0.25)  # 25th quantile
    Q3 = df[col].quantile(0.75)  # 75th quantile
    IQR = Q3 - Q1
    Lower_Whisker = Q1 - 1.5 * IQR
    Upper_Whisker = Q3 + 1.5 * IQR

    # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker
    # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker
    df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)

    return df


def treat_outliers_all(df, col_list):
    """
    Treat outliers in a list of variables

    df: dataframe
    col_list: list of dataframe columns
    """
    for c in col_list:
        df = treat_outliers(df, c)

    return df

cols_list = ("no_of_week_nights", 'no_of_weekend_nights', 'lead_time', 'avg_price_per_room')
ldata = treat_outliers_all(ldata, cols_list)

# let's look at box plot to see if outliers have been treated or not
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(ldata[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

replaceStruct = {
                "type_of_meal_plan":     {"Meal Plan 1": 1, "Meal Plan 2": 2 ,"Meal Plan 3": 3 ,"Not Selected":-1},
                "room_type_reserved": {"Room_Type 1": 1, "Room_Type 2":2 , "Room_Type 3": 3, "Room_Type 4": 4,"Room_Type 5": 5,
                                      "Room_Type 6": 6,"Room_Type 7": 7},
                 "market_segment_type": {"Online": 1, "Offline":2 , "Corporate": 3, "Complementary": 4,"Aviation": 5},
                 "booking_status": {"Not_Canceled":1, "Canceled":0}}
oneHotCols=["type_of_meal_plan","room_type_reserved","market_segment_type", "booking_status"]
ldata=data.replace(replaceStruct)
ldata=pd.get_dummies(data, columns=oneHotCols)
ldata.head(10)

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	...	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Aviation	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online	booking_status_Canceled	booking_status_Not_Canceled
0	2	0	1	2	0	224	0	0	0	65.00	...	0	0	0	0	0	0	1	0	0	1
1	2	0	2	3	0	5	0	0	0	106.68	...	0	0	0	0	0	0	0	1	0	1
2	1	0	2	1	0	1	0	0	0	60.00	...	0	0	0	0	0	0	0	1	1	0
3	2	0	0	2	0	211	0	0	0	100.00	...	0	0	0	0	0	0	0	1	1	0
4	2	0	1	1	0	48	0	0	0	94.50	...	0	0	0	0	0	0	0	1	1	0
5	2	0	0	2	0	346	0	0	0	115.00	...	0	0	0	0	0	0	0	1	1	0
6	2	0	1	3	0	34	0	0	0	107.55	...	0	0	0	0	0	0	0	1	0	1
7	2	0	1	3	0	83	0	0	0	105.61	...	0	0	0	0	0	0	0	1	0	1
8	3	0	0	4	0	121	0	0	0	96.90	...	0	0	0	0	0	0	1	0	0	1
9	2	0	0	5	0	44	0	0	0	133.44	...	0	0	0	0	0	0	0	1	0	1

10 rows × 31 columns

ldata["date"] = ldata["arrival_year_date"].values.astype(float)
ldata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 31 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          25965 non-null  int64         
 1   no_of_children                        25965 non-null  int64         
 2   no_of_weekend_nights                  25965 non-null  int64         
 3   no_of_week_nights                     25965 non-null  int64         
 4   required_car_parking_space            25965 non-null  int64         
 5   lead_time                             25965 non-null  int64         
 6   repeated_guest                        25965 non-null  int64         
 7   no_of_previous_cancellations          25965 non-null  int64         
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64         
 9   avg_price_per_room                    25965 non-null  float64       
 10  no_of_special_requests                25965 non-null  int64         
 11  arrival_year_date                     25965 non-null  datetime64[ns]
 12  date                                  25965 non-null  float64       
 13  type_of_meal_plan_Meal Plan 1         25965 non-null  uint8         
 14  type_of_meal_plan_Meal Plan 2         25965 non-null  uint8         
 15  type_of_meal_plan_Meal Plan 3         25965 non-null  uint8         
 16  type_of_meal_plan_Not Selected        25965 non-null  uint8         
 17  room_type_reserved_Room_Type 1        25965 non-null  uint8         
 18  room_type_reserved_Room_Type 2        25965 non-null  uint8         
 19  room_type_reserved_Room_Type 3        25965 non-null  uint8         
 20  room_type_reserved_Room_Type 4        25965 non-null  uint8         
 21  room_type_reserved_Room_Type 5        25965 non-null  uint8         
 22  room_type_reserved_Room_Type 6        25965 non-null  uint8         
 23  room_type_reserved_Room_Type 7        25965 non-null  uint8         
 24  market_segment_type_Aviation          25965 non-null  uint8         
 25  market_segment_type_Complementary     25965 non-null  uint8         
 26  market_segment_type_Corporate         25965 non-null  uint8         
 27  market_segment_type_Offline           25965 non-null  uint8         
 28  market_segment_type_Online            25965 non-null  uint8         
 29  booking_status_Canceled               25965 non-null  uint8         
 30  booking_status_Not_Canceled           25965 non-null  uint8         
dtypes: datetime64[ns](1), float64(2), int64(10), uint8(18)
memory usage: 4.2 MB

ldata.drop(['arrival_year_date', 'booking_status_Not_Canceled'], axis=1, inplace=True)
ldata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 29 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          25965 non-null  int64  
 1   no_of_children                        25965 non-null  int64  
 2   no_of_weekend_nights                  25965 non-null  int64  
 3   no_of_week_nights                     25965 non-null  int64  
 4   required_car_parking_space            25965 non-null  int64  
 5   lead_time                             25965 non-null  int64  
 6   repeated_guest                        25965 non-null  int64  
 7   no_of_previous_cancellations          25965 non-null  int64  
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64  
 9   avg_price_per_room                    25965 non-null  float64
 10  no_of_special_requests                25965 non-null  int64  
 11  date                                  25965 non-null  float64
 12  type_of_meal_plan_Meal Plan 1         25965 non-null  uint8  
 13  type_of_meal_plan_Meal Plan 2         25965 non-null  uint8  
 14  type_of_meal_plan_Meal Plan 3         25965 non-null  uint8  
 15  type_of_meal_plan_Not Selected        25965 non-null  uint8  
 16  room_type_reserved_Room_Type 1        25965 non-null  uint8  
 17  room_type_reserved_Room_Type 2        25965 non-null  uint8  
 18  room_type_reserved_Room_Type 3        25965 non-null  uint8  
 19  room_type_reserved_Room_Type 4        25965 non-null  uint8  
 20  room_type_reserved_Room_Type 5        25965 non-null  uint8  
 21  room_type_reserved_Room_Type 6        25965 non-null  uint8  
 22  room_type_reserved_Room_Type 7        25965 non-null  uint8  
 23  market_segment_type_Aviation          25965 non-null  uint8  
 24  market_segment_type_Complementary     25965 non-null  uint8  
 25  market_segment_type_Corporate         25965 non-null  uint8  
 26  market_segment_type_Offline           25965 non-null  uint8  
 27  market_segment_type_Online            25965 non-null  uint8  
 28  booking_status_Canceled               25965 non-null  uint8  
dtypes: float64(2), int64(10), uint8(17)
memory usage: 4.0 MB

print(data.type_of_meal_plan.value_counts())
print(data.room_type_reserved.value_counts())
print(data.market_segment_type.value_counts())
print(data.booking_status.value_counts())

pdata = data.copy()
pdata.head()

Meal Plan 1     20367
Not Selected     4462
Meal Plan 2      1131
Meal Plan 3         5
Name: type_of_meal_plan, dtype: int64
Room_Type 1    18617
Room_Type 4     5415
Room_Type 6      941
Room_Type 2      600
Room_Type 5      230
Room_Type 7      156
Room_Type 3        6
Name: room_type_reserved, dtype: int64
Online           19999
Offline           4104
Corporate         1406
Complementary      356
Aviation           100
Name: market_segment_type, dtype: int64
Not_Canceled    18530
Canceled         7435
Name: booking_status, dtype: int64

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status	arrival_year_date	date
0	2	0	1	2	Meal Plan 1	0	Room_Type 1	224	Offline	0	0	0	65.00	0	Not_Canceled	2017-10-02	1.506902e+18
1	2	0	2	3	Not Selected	0	Room_Type 1	5	Online	0	0	0	106.68	1	Not_Canceled	2018-11-06	1.541462e+18
2	1	0	2	1	Meal Plan 1	0	Room_Type 1	1	Online	0	0	0	60.00	0	Canceled	2018-02-28	1.519776e+18
3	2	0	0	2	Meal Plan 1	0	Room_Type 1	211	Online	0	0	0	100.00	0	Canceled	2018-05-20	1.526774e+18
4	2	0	1	1	Not Selected	0	Room_Type 1	48	Online	0	0	0	94.50	0	Canceled	2018-04-11	1.523405e+18

replaceStruct = {
                "type_of_meal_plan":     {"Meal Plan 1": 1, "Meal Plan 2": 2 ,"Meal Plan 3": 3 ,"Not Selected":-1},
                "room_type_reserved": {"Room_Type 1": 1, "Room_Type 2":2 , "Room_Type 3": 3, "Room_Type 4": 4,"Room_Type 5": 5,
                                      "Room_Type 6": 6,"Room_Type 7": 7},
                 "market_segment_type": {"Online": 1, "Offline":2 , "Corporate": 3, "Complementary": 4,"Aviation": 5},
                 "booking_status": {"Not_Canceled":1, "Canceled":0}}
oneHotCols=["type_of_meal_plan","room_type_reserved","market_segment_type", "booking_status"]
pdata=data.replace(replaceStruct)
pdata=pd.get_dummies(data, columns=oneHotCols)
pdata.head(10)

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	arrival_year_date	date	type_of_meal_plan_Meal Plan 1	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 1	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Aviation	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online	booking_status_Canceled	booking_status_Not_Canceled
0	2	0	1	2	0	224	0	0	0	65.00	0	2017-10-02	1.506902e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1
1	2	0	2	3	0	5	0	0	0	106.68	1	2018-11-06	1.541462e+18	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	0	1
2	1	0	2	1	0	1	0	0	0	60.00	0	2018-02-28	1.519776e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0
3	2	0	0	2	0	211	0	0	0	100.00	0	2018-05-20	1.526774e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0
4	2	0	1	1	0	48	0	0	0	94.50	0	2018-04-11	1.523405e+18	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0
5	2	0	0	2	0	346	0	0	0	115.00	1	2018-09-13	1.536797e+18	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0
6	2	0	1	3	0	34	0	0	0	107.55	1	2017-10-15	1.508026e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1
7	2	0	1	3	0	83	0	0	0	105.61	1	2018-12-26	1.545782e+18	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1
8	3	0	0	4	0	121	0	0	0	96.90	1	2018-07-06	1.530835e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1
9	2	0	0	5	0	44	0	0	0	133.44	3	2018-10-18	1.539821e+18	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1

This breaks out all the categorical variables and dropped the booking status column since that is the dependent variable.

pdata["date"] = pdata["arrival_year_date"].values.astype(float)
pdata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 31 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          25965 non-null  int64         
 1   no_of_children                        25965 non-null  int64         
 2   no_of_weekend_nights                  25965 non-null  int64         
 3   no_of_week_nights                     25965 non-null  int64         
 4   required_car_parking_space            25965 non-null  int64         
 5   lead_time                             25965 non-null  int64         
 6   repeated_guest                        25965 non-null  int64         
 7   no_of_previous_cancellations          25965 non-null  int64         
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64         
 9   avg_price_per_room                    25965 non-null  float64       
 10  no_of_special_requests                25965 non-null  int64         
 11  arrival_year_date                     25965 non-null  datetime64[ns]
 12  date                                  25965 non-null  float64       
 13  type_of_meal_plan_Meal Plan 1         25965 non-null  uint8         
 14  type_of_meal_plan_Meal Plan 2         25965 non-null  uint8         
 15  type_of_meal_plan_Meal Plan 3         25965 non-null  uint8         
 16  type_of_meal_plan_Not Selected        25965 non-null  uint8         
 17  room_type_reserved_Room_Type 1        25965 non-null  uint8         
 18  room_type_reserved_Room_Type 2        25965 non-null  uint8         
 19  room_type_reserved_Room_Type 3        25965 non-null  uint8         
 20  room_type_reserved_Room_Type 4        25965 non-null  uint8         
 21  room_type_reserved_Room_Type 5        25965 non-null  uint8         
 22  room_type_reserved_Room_Type 6        25965 non-null  uint8         
 23  room_type_reserved_Room_Type 7        25965 non-null  uint8         
 24  market_segment_type_Aviation          25965 non-null  uint8         
 25  market_segment_type_Complementary     25965 non-null  uint8         
 26  market_segment_type_Corporate         25965 non-null  uint8         
 27  market_segment_type_Offline           25965 non-null  uint8         
 28  market_segment_type_Online            25965 non-null  uint8         
 29  booking_status_Canceled               25965 non-null  uint8         
 30  booking_status_Not_Canceled           25965 non-null  uint8         
dtypes: datetime64[ns](1), float64(2), int64(10), uint8(18)
memory usage: 4.2 MB

pdata.head()

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	arrival_year_date	date	type_of_meal_plan_Meal Plan 1	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 1	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Aviation	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online	booking_status_Canceled	booking_status_Not_Canceled
0	2	0	1	2	0	224	0	0	0	65.00	0	2017-10-02	1.506902e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1
1	2	0	2	3	0	5	0	0	0	106.68	1	2018-11-06	1.541462e+18	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	0	1
2	1	0	2	1	0	1	0	0	0	60.00	0	2018-02-28	1.519776e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0
3	2	0	0	2	0	211	0	0	0	100.00	0	2018-05-20	1.526774e+18	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0
4	2	0	1	1	0	48	0	0	0	94.50	0	2018-04-11	1.523405e+18	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	0

pdata.drop(['arrival_year_date', 'booking_status_Canceled'], axis=1, inplace=True)
pdata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 29 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          25965 non-null  int64  
 1   no_of_children                        25965 non-null  int64  
 2   no_of_weekend_nights                  25965 non-null  int64  
 3   no_of_week_nights                     25965 non-null  int64  
 4   required_car_parking_space            25965 non-null  int64  
 5   lead_time                             25965 non-null  int64  
 6   repeated_guest                        25965 non-null  int64  
 7   no_of_previous_cancellations          25965 non-null  int64  
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64  
 9   avg_price_per_room                    25965 non-null  float64
 10  no_of_special_requests                25965 non-null  int64  
 11  date                                  25965 non-null  float64
 12  type_of_meal_plan_Meal Plan 1         25965 non-null  uint8  
 13  type_of_meal_plan_Meal Plan 2         25965 non-null  uint8  
 14  type_of_meal_plan_Meal Plan 3         25965 non-null  uint8  
 15  type_of_meal_plan_Not Selected        25965 non-null  uint8  
 16  room_type_reserved_Room_Type 1        25965 non-null  uint8  
 17  room_type_reserved_Room_Type 2        25965 non-null  uint8  
 18  room_type_reserved_Room_Type 3        25965 non-null  uint8  
 19  room_type_reserved_Room_Type 4        25965 non-null  uint8  
 20  room_type_reserved_Room_Type 5        25965 non-null  uint8  
 21  room_type_reserved_Room_Type 6        25965 non-null  uint8  
 22  room_type_reserved_Room_Type 7        25965 non-null  uint8  
 23  market_segment_type_Aviation          25965 non-null  uint8  
 24  market_segment_type_Complementary     25965 non-null  uint8  
 25  market_segment_type_Corporate         25965 non-null  uint8  
 26  market_segment_type_Offline           25965 non-null  uint8  
 27  market_segment_type_Online            25965 non-null  uint8  
 28  booking_status_Canceled               25965 non-null  uint8  
dtypes: float64(2), int64(10), uint8(17)
memory usage: 4.0 MB

ldata.head()

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	...	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Aviation	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online	booking_status_Canceled
0	2	0	1	2	0	224	0	0	0	65.00	...	0	0	0	0	0	0	0	1	0	0
1	2	0	2	3	0	5	0	0	0	106.68	...	0	0	0	0	0	0	0	0	1	0
2	1	0	2	1	0	1	0	0	0	60.00	...	0	0	0	0	0	0	0	0	1	1
3	2	0	0	2	0	211	0	0	0	100.00	...	0	0	0	0	0	0	0	0	1	1
4	2	0	1	1	0	48	0	0	0	94.50	...	0	0	0	0	0	0	0	0	1	1

5 rows × 29 columns

tdata = data.copy()
meal_plan = pd.get_dummies(tdata['type_of_meal_plan'], drop_first=True)
room_type = pd.get_dummies(tdata['room_type_reserved'], drop_first=True)
market_segment = pd.get_dummies(tdata['market_segment_type'], drop_first=True)
booking_status = pd.get_dummies(tdata['booking_status'], drop_first=True)

tdata.drop(['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status', 'arrival_year_date'], axis=1, inplace=True)

tdata = pd.concat((tdata, meal_plan, room_type, market_segment, booking_status), axis=1)
tdata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 26 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          25965 non-null  int64  
 1   no_of_children                        25965 non-null  int64  
 2   no_of_weekend_nights                  25965 non-null  int64  
 3   no_of_week_nights                     25965 non-null  int64  
 4   required_car_parking_space            25965 non-null  int64  
 5   lead_time                             25965 non-null  int64  
 6   repeated_guest                        25965 non-null  int64  
 7   no_of_previous_cancellations          25965 non-null  int64  
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64  
 9   avg_price_per_room                    25965 non-null  float64
 10  no_of_special_requests                25965 non-null  int64  
 11  date                                  25965 non-null  float64
 12  Meal Plan 2                           25965 non-null  uint8  
 13  Meal Plan 3                           25965 non-null  uint8  
 14  Not Selected                          25965 non-null  uint8  
 15  Room_Type 2                           25965 non-null  uint8  
 16  Room_Type 3                           25965 non-null  uint8  
 17  Room_Type 4                           25965 non-null  uint8  
 18  Room_Type 5                           25965 non-null  uint8  
 19  Room_Type 6                           25965 non-null  uint8  
 20  Room_Type 7                           25965 non-null  uint8  
 21  Complementary                         25965 non-null  uint8  
 22  Corporate                             25965 non-null  uint8  
 23  Offline                               25965 non-null  uint8  
 24  Online                                25965 non-null  uint8  
 25  Not_Canceled                          25965 non-null  uint8  
dtypes: float64(2), int64(10), uint8(14)
memory usage: 3.9 MB

Building a Logistic Regression model

X = tdata.drop(["Not_Canceled"], axis=1)
Y = tdata["Not_Canceled"]

# adding a constant to X variable
X = add_constant(X)

# creating dummies
X = pd.get_dummies(X, drop_first=True)


# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1, stratify=Y
)

The Stratify argument maintains the original distribution of classes in the target variable while splitting the data into train and test sets.

# fitting the model on training set
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit()

Warning: Maximum number of iterations has been exceeded.
         Current function value: inf
         Iterations: 35

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

# let's print the logistic regression summary
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18149
Method:                           MLE   Df Model:                           25
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:15:19   Log-Likelihood:                   -inf
converged:                      False   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    3.7360      3.215      1.162      0.245      -2.566      10.038
no_of_adults                             0.0012      0.046      0.025      0.980      -0.089       0.092
no_of_children                          -0.1562      0.061     -2.581      0.010      -0.275      -0.038
no_of_weekend_nights                    -0.0472      0.023     -2.013      0.044      -0.093      -0.001
no_of_week_nights                       -0.0981      0.014     -7.055      0.000      -0.125      -0.071
required_car_parking_space               1.4909      0.139     10.695      0.000       1.218       1.764
lead_time                               -0.0158      0.000    -42.927      0.000      -0.016      -0.015
repeated_guest                           2.7200      0.727      3.744      0.000       1.296       4.144
no_of_previous_cancellations            -0.0403      0.243     -0.166      0.868      -0.518       0.437
no_of_previous_bookings_not_canceled     0.0473      0.127      0.373      0.709      -0.201       0.296
avg_price_per_room                      -0.0163      0.001    -18.764      0.000      -0.018      -0.015
no_of_special_requests                   1.4799      0.034     44.147      0.000       1.414       1.546
date                                 -8.251e-19    2.1e-18     -0.392      0.695   -4.95e-18     3.3e-18
Meal Plan 2                              0.0608      0.107      0.567      0.570      -0.149       0.271
Meal Plan 3                            -11.7872    193.550     -0.061      0.951    -391.139     367.564
Not Selected                            -0.2818      0.058     -4.857      0.000      -0.396      -0.168
Room_Type 2                              0.2590      0.146      1.775      0.076      -0.027       0.545
Room_Type 3                             -0.2673      2.830     -0.094      0.925      -5.814       5.279
Room_Type 4                              0.0896      0.058      1.545      0.122      -0.024       0.203
Room_Type 5                              0.7380      0.250      2.950      0.003       0.248       1.228
Room_Type 6                              0.7452      0.153      4.881      0.000       0.446       1.044
Room_Type 7                              1.2699      0.326      3.897      0.000       0.631       1.909
Complementary                           34.9279   5.58e+05   6.26e-05      1.000   -1.09e+06    1.09e+06
Corporate                                0.9929      0.325      3.055      0.002       0.356       1.630
Offline                                  2.7508      0.315      8.731      0.000       2.133       3.368
Online                                   0.3609      0.306      1.178      0.239      -0.239       0.961
========================================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

• The coefficients of the logistic regression model are in terms of log(odd).
• P>|z|: The interpretation of p-values is similar to that in Linear Regression.

# predicting on training set
# default threshold is 0.5, if predicted probability is greater than 0.5 the observation will be classified as 1

pred_train = lg.predict(X_train) > 0.5
pred_train = np.round(pred_train)

cm = confusion_matrix(y_train, pred_train)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()

The confusion matrix

• True Positives (TP): A person canceled and the model predicted person canceled.
• True Negatives (TN): A person did not cancel and the model predicted person did not cancel.
• False Positives (FP): The model predicted person canceled but the person did not cancel.
• False Negatives (FN): The model predicted person did not cancel but the person canceled.

print("Accuracy on training set : ", accuracy_score(y_train, pred_train))

Accuracy on training set :  0.8078679504814306

Checking Multicollinearity

• In order to make statistical inferences from a logistic regression model, it is important to ensure that there is no multicollinearity present in the data.

# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                   0.000000
no_of_adults                            1.004615
no_of_children                          1.000130
no_of_weekend_nights                    1.000457
no_of_week_nights                       1.000624
required_car_parking_space              0.999985
lead_time                               1.003908
repeated_guest                          0.999965
no_of_previous_cancellations            0.999975
no_of_previous_bookings_not_canceled    1.000035
avg_price_per_room                      1.008965
no_of_special_requests                  1.001272
date                                    1.228558
Meal Plan 2                             0.999966
Meal Plan 3                             0.999997
Not Selected                            1.000715
Room_Type 2                             0.999929
Room_Type 3                             0.999999
Room_Type 4                             1.000670
Room_Type 5                             1.000030
Room_Type 6                             1.000032
Room_Type 7                             1.000024
Complementary                           0.999907
Corporate                               0.999816
Offline                                 0.999413
Online                                  1.002986
dtype: float64

• We observe that there is no multicollinearity present in the data.
• Hence, The values of coefficient and p-values are reliable.
• I will remove the insignificant variables (p-value > 0.05) from data.

# summary of initial logistic regression model
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18149
Method:                           MLE   Df Model:                           25
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:15:39   Log-Likelihood:                   -inf
converged:                      False   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    3.7360      3.215      1.162      0.245      -2.566      10.038
no_of_adults                             0.0012      0.046      0.025      0.980      -0.089       0.092
no_of_children                          -0.1562      0.061     -2.581      0.010      -0.275      -0.038
no_of_weekend_nights                    -0.0472      0.023     -2.013      0.044      -0.093      -0.001
no_of_week_nights                       -0.0981      0.014     -7.055      0.000      -0.125      -0.071
required_car_parking_space               1.4909      0.139     10.695      0.000       1.218       1.764
lead_time                               -0.0158      0.000    -42.927      0.000      -0.016      -0.015
repeated_guest                           2.7200      0.727      3.744      0.000       1.296       4.144
no_of_previous_cancellations            -0.0403      0.243     -0.166      0.868      -0.518       0.437
no_of_previous_bookings_not_canceled     0.0473      0.127      0.373      0.709      -0.201       0.296
avg_price_per_room                      -0.0163      0.001    -18.764      0.000      -0.018      -0.015
no_of_special_requests                   1.4799      0.034     44.147      0.000       1.414       1.546
date                                 -8.251e-19    2.1e-18     -0.392      0.695   -4.95e-18     3.3e-18
Meal Plan 2                              0.0608      0.107      0.567      0.570      -0.149       0.271
Meal Plan 3                            -11.7872    193.550     -0.061      0.951    -391.139     367.564
Not Selected                            -0.2818      0.058     -4.857      0.000      -0.396      -0.168
Room_Type 2                              0.2590      0.146      1.775      0.076      -0.027       0.545
Room_Type 3                             -0.2673      2.830     -0.094      0.925      -5.814       5.279
Room_Type 4                              0.0896      0.058      1.545      0.122      -0.024       0.203
Room_Type 5                              0.7380      0.250      2.950      0.003       0.248       1.228
Room_Type 6                              0.7452      0.153      4.881      0.000       0.446       1.044
Room_Type 7                              1.2699      0.326      3.897      0.000       0.631       1.909
Complementary                           34.9279   5.58e+05   6.26e-05      1.000   -1.09e+06    1.09e+06
Corporate                                0.9929      0.325      3.055      0.002       0.356       1.630
Offline                                  2.7508      0.315      8.731      0.000       2.133       3.368
Online                                   0.3609      0.306      1.178      0.239      -0.239       0.961
========================================================================================================

Dropping the highest p-value Complementary.

X_train1  = X_train.drop("Complementary", axis=1)

# fitting the model on training set
logit1 = sm.Logit(y_train, X_train1.astype(float))
lg1 = logit1.fit()

pred_train1 = lg1.predict(X_train1)
pred_train1 = np.round(pred_train1)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg1.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 11
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18150
Method:                           MLE   Df Model:                           24
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:15:59   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    4.3929      3.207      1.370      0.171      -1.892      10.678
no_of_adults                             0.0141      0.046      0.306      0.760      -0.076       0.104
no_of_children                          -0.1497      0.061     -2.473      0.013      -0.268      -0.031
no_of_weekend_nights                    -0.0500      0.023     -2.132      0.033      -0.096      -0.004
no_of_week_nights                       -0.0995      0.014     -7.159      0.000      -0.127      -0.072
required_car_parking_space               1.4918      0.139     10.696      0.000       1.218       1.765
lead_time                               -0.0158      0.000    -43.024      0.000      -0.016      -0.015
repeated_guest                           2.6692      0.730      3.656      0.000       1.238       4.100
no_of_previous_cancellations            -0.0396      0.242     -0.164      0.870      -0.514       0.435
no_of_previous_bookings_not_canceled     0.0508      0.129      0.393      0.694      -0.202       0.304
avg_price_per_room                      -0.0167      0.001    -19.434      0.000      -0.018      -0.015
no_of_special_requests                   1.4831      0.034     44.242      0.000       1.417       1.549
date                                 -8.124e-19    2.1e-18     -0.387      0.699   -4.93e-18     3.3e-18
Meal Plan 2                              0.0730      0.107      0.682      0.496      -0.137       0.283
Meal Plan 3                             -2.6054      1.750     -1.489      0.137      -6.035       0.824
Not Selected                            -0.2883      0.058     -4.969      0.000      -0.402      -0.175
Room_Type 2                              0.2583      0.146      1.771      0.077      -0.028       0.544
Room_Type 3                             -0.2609      2.835     -0.092      0.927      -5.816       5.295
Room_Type 4                              0.0878      0.058      1.513      0.130      -0.026       0.201
Room_Type 5                              0.7576      0.250      3.035      0.002       0.268       1.247
Room_Type 6                              0.7610      0.153      4.985      0.000       0.462       1.060
Room_Type 7                              1.3123      0.325      4.034      0.000       0.675       1.950
Corporate                                0.3411      0.294      1.161      0.246      -0.235       0.917
Offline                                  2.0919      0.281      7.444      0.000       1.541       2.643
Online                                  -0.2907      0.273     -1.066      0.286      -0.825       0.244
========================================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value no_of_previous_bookings_not_canceled.

X_train2  = X_train1.drop("Room_Type 3", axis=1)

# fitting the model on training set
logit2 = sm.Logit(y_train, X_train2.astype(float))
lg2 = logit2.fit()

pred_train2 = lg2.predict(X_train2)
pred_train2 = np.round(pred_train2)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg2.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 11
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18151
Method:                           MLE   Df Model:                           23
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:16:39   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    4.3919      3.207      1.370      0.171      -1.893      10.677
no_of_adults                             0.0141      0.046      0.307      0.759      -0.076       0.104
no_of_children                          -0.1496      0.061     -2.473      0.013      -0.268      -0.031
no_of_weekend_nights                    -0.0500      0.023     -2.132      0.033      -0.096      -0.004
no_of_week_nights                       -0.0995      0.014     -7.159      0.000      -0.127      -0.072
required_car_parking_space               1.4918      0.139     10.696      0.000       1.218       1.765
lead_time                               -0.0158      0.000    -43.026      0.000      -0.016      -0.015
repeated_guest                           2.6692      0.730      3.656      0.000       1.238       4.100
no_of_previous_cancellations            -0.0396      0.242     -0.164      0.870      -0.514       0.435
no_of_previous_bookings_not_canceled     0.0508      0.129      0.393      0.694      -0.202       0.304
avg_price_per_room                      -0.0167      0.001    -19.435      0.000      -0.018      -0.015
no_of_special_requests                   1.4831      0.034     44.243      0.000       1.417       1.549
date                                 -8.118e-19    2.1e-18     -0.386      0.699   -4.93e-18    3.31e-18
Meal Plan 2                              0.0731      0.107      0.682      0.495      -0.137       0.283
Meal Plan 3                             -2.6054      1.750     -1.489      0.137      -6.035       0.825
Not Selected                            -0.2883      0.058     -4.968      0.000      -0.402      -0.175
Room_Type 2                              0.2583      0.146      1.771      0.077      -0.028       0.544
Room_Type 4                              0.0878      0.058      1.514      0.130      -0.026       0.201
Room_Type 5                              0.7576      0.250      3.035      0.002       0.268       1.247
Room_Type 6                              0.7611      0.153      4.986      0.000       0.462       1.060
Room_Type 7                              1.3123      0.325      4.034      0.000       0.675       1.950
Corporate                                0.3411      0.294      1.161      0.246      -0.235       0.917
Offline                                  2.0918      0.281      7.444      0.000       1.541       2.643
Online                                  -0.2907      0.273     -1.066      0.286      -0.825       0.244
========================================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value no_of_previous_cancellations.

X_train3  = X_train2.drop("no_of_previous_cancellations", axis=1)

# fitting the model on training set
logit3 = sm.Logit(y_train, X_train3.astype(float))
lg3 = logit3.fit()

pred_train3 = lg3.predict(X_train3)
pred_train3 = np.round(pred_train3)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg3.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 11
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18152
Method:                           MLE   Df Model:                           22
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:22:35   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    4.3869      3.207      1.368      0.171      -1.898      10.672
no_of_adults                             0.0141      0.046      0.307      0.759      -0.076       0.104
no_of_children                          -0.1496      0.061     -2.473      0.013      -0.268      -0.031
no_of_weekend_nights                    -0.0500      0.023     -2.131      0.033      -0.096      -0.004
no_of_week_nights                       -0.0995      0.014     -7.159      0.000      -0.127      -0.072
required_car_parking_space               1.4919      0.139     10.696      0.000       1.218       1.765
lead_time                               -0.0158      0.000    -43.026      0.000      -0.016      -0.015
repeated_guest                           2.6514      0.717      3.699      0.000       1.247       4.056
no_of_previous_bookings_not_canceled     0.0453      0.123      0.369      0.712      -0.196       0.286
avg_price_per_room                      -0.0167      0.001    -19.435      0.000      -0.018      -0.015
no_of_special_requests                   1.4831      0.034     44.243      0.000       1.417       1.549
date                                 -8.084e-19    2.1e-18     -0.385      0.700   -4.93e-18    3.31e-18
Meal Plan 2                              0.0731      0.107      0.682      0.495      -0.137       0.283
Meal Plan 3                             -2.6052      1.750     -1.489      0.137      -6.035       0.825
Not Selected                            -0.2883      0.058     -4.969      0.000      -0.402      -0.175
Room_Type 2                              0.2584      0.146      1.771      0.076      -0.028       0.544
Room_Type 4                              0.0878      0.058      1.514      0.130      -0.026       0.201
Room_Type 5                              0.7576      0.250      3.035      0.002       0.268       1.247
Room_Type 6                              0.7611      0.153      4.986      0.000       0.462       1.060
Room_Type 7                              1.3123      0.325      4.034      0.000       0.675       1.950
Corporate                                0.3415      0.294      1.163      0.245      -0.234       0.917
Offline                                  2.0915      0.281      7.443      0.000       1.541       2.642
Online                                  -0.2910      0.273     -1.067      0.286      -0.825       0.243
========================================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value no_of_adults.

X_train4  = X_train3.drop("no_of_adults", axis=1)

# fitting the model on training set
logit4 = sm.Logit(y_train, X_train4.astype(float))
lg4 = logit4.fit()

pred_train4 = lg4.predict(X_train4)
pred_train4 = np.round(pred_train4)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg4.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 11
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18153
Method:                           MLE   Df Model:                           21
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        11:23:33   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                    4.4396      3.202      1.387      0.166      -1.836      10.715
no_of_children                          -0.1522      0.060     -2.541      0.011      -0.270      -0.035
no_of_weekend_nights                    -0.0499      0.023     -2.127      0.033      -0.096      -0.004
no_of_week_nights                       -0.0994      0.014     -7.157      0.000      -0.127      -0.072
required_car_parking_space               1.4928      0.139     10.704      0.000       1.219       1.766
lead_time                               -0.0158      0.000    -43.389      0.000      -0.016      -0.015
repeated_guest                           2.6497      0.717      3.697      0.000       1.245       4.054
no_of_previous_bookings_not_canceled     0.0454      0.123      0.369      0.712      -0.196       0.286
avg_price_per_room                      -0.0166      0.001    -19.813      0.000      -0.018      -0.015
no_of_special_requests                   1.4838      0.033     44.362      0.000       1.418       1.549
date                                 -8.353e-19    2.1e-18     -0.398      0.691   -4.95e-18    3.28e-18
Meal Plan 2                              0.0717      0.107      0.670      0.503      -0.138       0.282
Meal Plan 3                             -2.6014      1.746     -1.490      0.136      -6.023       0.821
Not Selected                            -0.2871      0.058     -4.959      0.000      -0.401      -0.174
Room_Type 2                              0.2558      0.146      1.757      0.079      -0.029       0.541
Room_Type 4                              0.0918      0.057      1.623      0.105      -0.019       0.203
Room_Type 5                              0.7568      0.249      3.034      0.002       0.268       1.246
Room_Type 6                              0.7631      0.152      5.006      0.000       0.464       1.062
Room_Type 7                              1.3182      0.325      4.060      0.000       0.682       1.955
Corporate                                0.3415      0.294      1.163      0.245      -0.234       0.917
Offline                                  2.0989      0.280      7.501      0.000       1.550       2.647
Online                                  -0.2838      0.271     -1.045      0.296      -0.816       0.248
========================================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value no_of_previous_bookings_not_canceled.

X_train5  = X_train4.drop("no_of_previous_bookings_not_canceled", axis=1)

# fitting the model on training set
logit5 = sm.Logit(y_train, X_train5.astype(float))
lg5 = logit5.fit()

pred_train5 = lg5.predict(X_train5)
pred_train5 = np.round(pred_train5)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg5.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18154
Method:                           MLE   Df Model:                           20
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:41:17   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          4.4248      3.202      1.382      0.167      -1.850      10.700
no_of_children                -0.1522      0.060     -2.541      0.011      -0.270      -0.035
no_of_weekend_nights          -0.0499      0.023     -2.126      0.033      -0.096      -0.004
no_of_week_nights             -0.0994      0.014     -7.157      0.000      -0.127      -0.072
required_car_parking_space     1.4928      0.139     10.704      0.000       1.219       1.766
lead_time                     -0.0158      0.000    -43.390      0.000      -0.016      -0.015
repeated_guest                 2.8337      0.588      4.819      0.000       1.681       3.986
avg_price_per_room            -0.0166      0.001    -19.816      0.000      -0.018      -0.015
no_of_special_requests         1.4838      0.033     44.364      0.000       1.418       1.549
date                       -8.264e-19    2.1e-18     -0.394      0.694   -4.94e-18    3.29e-18
Meal Plan 2                    0.0718      0.107      0.671      0.502      -0.138       0.282
Meal Plan 3                   -2.6012      1.746     -1.490      0.136      -6.024       0.822
Not Selected                  -0.2872      0.058     -4.961      0.000      -0.401      -0.174
Room_Type 2                    0.2558      0.146      1.757      0.079      -0.030       0.541
Room_Type 4                    0.0917      0.057      1.622      0.105      -0.019       0.203
Room_Type 5                    0.7568      0.249      3.034      0.002       0.268       1.246
Room_Type 6                    0.7633      0.152      5.007      0.000       0.465       1.062
Room_Type 7                    1.3184      0.325      4.060      0.000       0.682       1.955
Corporate                      0.3442      0.294      1.172      0.241      -0.231       0.920
Offline                        2.1004      0.280      7.506      0.000       1.552       2.649
Online                        -0.2822      0.271     -1.040      0.299      -0.814       0.250
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value date.

X_train6  = X_train5.drop("date", axis=1)

# fitting the model on training set
logit6 = sm.Logit(y_train, X_train6.astype(float))
lg6 = logit6.fit()

pred_train6 = lg6.predict(X_train6)
pred_train6 = np.round(pred_train6)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg6.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18155
Method:                           MLE   Df Model:                           19
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:43:53   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          3.1685      0.272     11.629      0.000       2.634       3.703
no_of_children                -0.1525      0.060     -2.547      0.011      -0.270      -0.035
no_of_weekend_nights          -0.0500      0.023     -2.132      0.033      -0.096      -0.004
no_of_week_nights             -0.0993      0.014     -7.151      0.000      -0.127      -0.072
required_car_parking_space     1.4955      0.139     10.738      0.000       1.223       1.769
lead_time                     -0.0158      0.000    -45.639      0.000      -0.016      -0.015
repeated_guest                 2.8300      0.588      4.814      0.000       1.678       3.982
avg_price_per_room            -0.0167      0.001    -20.739      0.000      -0.018      -0.015
no_of_special_requests         1.4830      0.033     44.437      0.000       1.418       1.548
Meal Plan 2                    0.0746      0.107      0.698      0.485      -0.135       0.284
Meal Plan 3                   -2.5909      1.754     -1.477      0.140      -6.028       0.847
Not Selected                  -0.2915      0.057     -5.130      0.000      -0.403      -0.180
Room_Type 2                    0.2569      0.145      1.766      0.077      -0.028       0.542
Room_Type 4                    0.0909      0.057      1.609      0.108      -0.020       0.202
Room_Type 5                    0.7549      0.249      3.028      0.002       0.266       1.244
Room_Type 6                    0.7685      0.152      5.060      0.000       0.471       1.066
Room_Type 7                    1.3220      0.325      4.072      0.000       0.686       1.958
Corporate                      0.3515      0.293      1.199      0.230      -0.223       0.926
Offline                        2.1076      0.279      7.547      0.000       1.560       2.655
Online                        -0.2745      0.271     -1.014      0.311      -0.805       0.256
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value Meal Plan 2.

X_train7  = X_train6.drop("Meal Plan 2", axis=1)

# fitting the model on training set
logit7 = sm.Logit(y_train, X_train7.astype(float))
lg7 = logit7.fit()

pred_train7 = lg7.predict(X_train7)
pred_train7 = np.round(pred_train7)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg7.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18156
Method:                           MLE   Df Model:                           18
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:52:14   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          3.1612      0.272     11.619      0.000       2.628       3.694
no_of_children                -0.1523      0.060     -2.544      0.011      -0.270      -0.035
no_of_weekend_nights          -0.0496      0.023     -2.115      0.034      -0.096      -0.004
no_of_week_nights             -0.0991      0.014     -7.135      0.000      -0.126      -0.072
required_car_parking_space     1.4954      0.139     10.737      0.000       1.222       1.768
lead_time                     -0.0158      0.000    -45.747      0.000      -0.016      -0.015
repeated_guest                 2.8314      0.588      4.816      0.000       1.679       3.984
avg_price_per_room            -0.0166      0.001    -21.102      0.000      -0.018      -0.015
no_of_special_requests         1.4828      0.033     44.431      0.000       1.417       1.548
Meal Plan 3                   -2.5917      1.742     -1.488      0.137      -6.005       0.822
Not Selected                  -0.2931      0.057     -5.161      0.000      -0.404      -0.182
Room_Type 2                    0.2545      0.145      1.751      0.080      -0.030       0.539
Room_Type 4                    0.0875      0.056      1.555      0.120      -0.023       0.198
Room_Type 5                    0.7498      0.249      3.008      0.003       0.261       1.238
Room_Type 6                    0.7596      0.151      5.019      0.000       0.463       1.056
Room_Type 7                    1.3061      0.324      4.035      0.000       0.672       1.940
Corporate                      0.3471      0.293      1.185      0.236      -0.227       0.921
Offline                        2.1124      0.279      7.569      0.000       1.565       2.659
Online                        -0.2794      0.271     -1.033      0.302      -0.810       0.251
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value Online.

X_train8  = X_train7.drop("Online", axis=1)

# fitting the model on training set
logit8 = sm.Logit(y_train, X_train8.astype(float))
lg8 = logit8.fit()

pred_train8 = lg8.predict(X_train8)
pred_train8 = np.round(pred_train8)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg8.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18157
Method:                           MLE   Df Model:                           17
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:54:01   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          2.9029      0.102     28.429      0.000       2.703       3.103
no_of_children                -0.1514      0.060     -2.529      0.011      -0.269      -0.034
no_of_weekend_nights          -0.0500      0.023     -2.130      0.033      -0.096      -0.004
no_of_week_nights             -0.0993      0.014     -7.154      0.000      -0.127      -0.072
required_car_parking_space     1.4979      0.139     10.754      0.000       1.225       1.771
lead_time                     -0.0158      0.000    -46.020      0.000      -0.016      -0.015
repeated_guest                 2.8522      0.587      4.855      0.000       1.701       4.003
avg_price_per_room            -0.0167      0.001    -21.618      0.000      -0.018      -0.015
no_of_special_requests         1.4820      0.033     44.418      0.000       1.417       1.547
Meal Plan 3                   -2.4576      1.673     -1.469      0.142      -5.737       0.822
Not Selected                  -0.2977      0.057     -5.259      0.000      -0.409      -0.187
Room_Type 2                    0.2524      0.145      1.736      0.083      -0.033       0.537
Room_Type 4                    0.0917      0.056      1.632      0.103      -0.018       0.202
Room_Type 5                    0.7552      0.249      3.032      0.002       0.267       1.243
Room_Type 6                    0.7663      0.151      5.068      0.000       0.470       1.063
Room_Type 7                    1.3190      0.323      4.078      0.000       0.685       1.953
Corporate                      0.6186      0.128      4.849      0.000       0.369       0.869
Offline                        2.3882      0.080     29.807      0.000       2.231       2.545
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value Online.

X_train9  = X_train8.drop("Meal Plan 3", axis=1)

# fitting the model on training set
logit9 = sm.Logit(y_train, X_train9.astype(float))
lg9 = logit9.fit()

pred_train9 = lg9.predict(X_train9)
pred_train9 = np.round(pred_train9)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg9.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18158
Method:                           MLE   Df Model:                           16
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:55:00   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          2.9030      0.102     28.396      0.000       2.703       3.103
no_of_children                -0.1506      0.060     -2.515      0.012      -0.268      -0.033
no_of_weekend_nights          -0.0497      0.023     -2.119      0.034      -0.096      -0.004
no_of_week_nights             -0.0992      0.014     -7.149      0.000      -0.126      -0.072
required_car_parking_space     1.4974      0.139     10.750      0.000       1.224       1.770
lead_time                     -0.0158      0.000    -46.014      0.000      -0.016      -0.015
repeated_guest                 2.8515      0.587      4.854      0.000       1.700       4.003
avg_price_per_room            -0.0168      0.001    -21.599      0.000      -0.018      -0.015
no_of_special_requests         1.4821      0.033     44.422      0.000       1.417       1.547
Not Selected                  -0.2975      0.057     -5.255      0.000      -0.408      -0.187
Room_Type 2                    0.2518      0.145      1.732      0.083      -0.033       0.537
Room_Type 4                    0.0922      0.056      1.641      0.101      -0.018       0.202
Room_Type 5                    0.7559      0.249      3.035      0.002       0.268       1.244
Room_Type 6                    0.7662      0.151      5.065      0.000       0.470       1.063
Room_Type 7                    1.3038      0.324      4.023      0.000       0.669       1.939
Corporate                      0.6191      0.128      4.853      0.000       0.369       0.869
Offline                        2.3853      0.080     29.794      0.000       2.228       2.542
==============================================================================================

Dropping the next highest p-value Online.

X_train10  = X_train9.drop("Room_Type 4", axis=1)

# fitting the model on training set
logit10 = sm.Logit(y_train, X_train10.astype(float))
lg10 = logit10.fit()

pred_train10 = lg10.predict(X_train10)
pred_train10 = np.round(pred_train10)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg10.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18159
Method:                           MLE   Df Model:                           15
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:56:12   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          2.8793      0.101     28.510      0.000       2.681       3.077
no_of_children                -0.1626      0.059     -2.746      0.006      -0.279      -0.047
no_of_weekend_nights          -0.0491      0.023     -2.095      0.036      -0.095      -0.003
no_of_week_nights             -0.0970      0.014     -7.025      0.000      -0.124      -0.070
required_car_parking_space     1.4915      0.139     10.710      0.000       1.219       1.764
lead_time                     -0.0158      0.000    -46.033      0.000      -0.016      -0.015
repeated_guest                 2.8628      0.587      4.874      0.000       1.712       4.014
avg_price_per_room            -0.0163      0.001    -22.535      0.000      -0.018      -0.015
no_of_special_requests         1.4814      0.033     44.413      0.000       1.416       1.547
Not Selected                  -0.3203      0.055     -5.834      0.000      -0.428      -0.213
Room_Type 2                    0.2365      0.145      1.632      0.103      -0.048       0.521
Room_Type 5                    0.7209      0.248      2.906      0.004       0.235       1.207
Room_Type 6                    0.7223      0.149      4.859      0.000       0.431       1.014
Room_Type 7                    1.2388      0.321      3.857      0.000       0.609       1.868
Corporate                      0.6052      0.127      4.757      0.000       0.356       0.855
Offline                        2.3740      0.080     29.800      0.000       2.218       2.530
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Dropping the next highest p-value Room_Type 2.

X_train11  = X_train10.drop("Room_Type 2", axis=1)

# fitting the model on training set
logit11 = sm.Logit(y_train, X_train11.astype(float))
lg11 = logit11.fit()

pred_train11 = lg11.predict(X_train11)
pred_train11 = np.round(pred_train11)


print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))
print(lg11.summary())

Optimization terminated successfully.
         Current function value: inf
         Iterations 9
Accuracy on training set :  0.8079779917469051
                           Logit Regression Results                           
==============================================================================
Dep. Variable:           Not_Canceled   No. Observations:                18175
Model:                          Logit   Df Residuals:                    18160
Method:                           MLE   Df Model:                           14
Date:                Fri, 13 May 2022   Pseudo R-squ.:                     inf
Time:                        15:57:40   Log-Likelihood:                   -inf
converged:                       True   LL-Null:                        0.0000
Covariance Type:            nonrobust   LLR p-value:                     1.000
==============================================================================================
                                 coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------
const                          2.8982      0.100     28.867      0.000       2.701       3.095
no_of_children                -0.1379      0.057     -2.400      0.016      -0.251      -0.025
no_of_weekend_nights          -0.0494      0.023     -2.107      0.035      -0.095      -0.003
no_of_week_nights             -0.0972      0.014     -7.042      0.000      -0.124      -0.070
required_car_parking_space     1.4972      0.139     10.748      0.000       1.224       1.770
lead_time                     -0.0158      0.000    -46.018      0.000      -0.016      -0.015
repeated_guest                 2.8604      0.587      4.870      0.000       1.709       4.012
avg_price_per_room            -0.0164      0.001    -22.822      0.000      -0.018      -0.015
no_of_special_requests         1.4798      0.033     44.400      0.000       1.415       1.545
Not Selected                  -0.3272      0.055     -5.978      0.000      -0.434      -0.220
Room_Type 5                    0.7172      0.248      2.892      0.004       0.231       1.203
Room_Type 6                    0.6835      0.147      4.654      0.000       0.396       0.971
Room_Type 7                    1.2232      0.321      3.816      0.000       0.595       1.852
Corporate                      0.5974      0.127      4.699      0.000       0.348       0.847
Offline                        2.3649      0.079     29.775      0.000       2.209       2.521
==============================================================================================

C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '
C:\Users\eliza\anaconda3\lib\site-packages\statsmodels\base\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  warnings.warn('Inverting hessian failed, no bse or cov_params '

Now no feature has p-value greater than 0.05, so we'll consider the features in X_train11 as the final ones and lg11 as final model.

Model performance evaluation

# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf
# this will help in making the Python code more structured automatically (good coding practice)
#%load_ext nb_black

import warnings

warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning

warnings.simplefilter("ignore", ConvergenceWarning)

# Libraries to help with reading and manipulating data

import pandas as pd
import numpy as np

# Library to split data
from sklearn.model_selection import train_test_split

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)


# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.linear_model import LogisticRegression


# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
    precision_recall_curve,
    roc_curve,
)

# predicting on training set
# default threshold is 0.5, if predicted probability is greater than 0.5 the observation will be classified as 1

pred_train = lg.predict(X_train) > 0.5
pred_train = np.round(pred_train)

cm = confusion_matrix(y_train, pred_train)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()

log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg11, X_train11, y_train
)

print("Training performance:")
log_reg_model_train_perf

Training performance:

	Accuracy	Recall	Precision	F1
0	0.808913	0.909953	0.836618	0.871746

# converting coefficients to odds
odds = np.exp(lg11.params)

# finding the percentage change
perc_change_odds = (np.exp(lg11.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train11.columns).T

	const	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	repeated_guest	avg_price_per_room	no_of_special_requests	Not Selected	Room_Type 5	Room_Type 6	Room_Type 7	Corporate	Offline
Odds	18.140936	0.871146	0.951800	0.907330	4.469278	0.984342	17.468578	0.983708	4.392193	0.720941	2.048695	1.980791	3.398057	1.817340	10.643323
Change_odd%	1714.093581	-12.885392	-4.820007	-9.266997	346.927831	-1.565844	1646.857828	-1.629182	339.219348	-27.905858	104.869540	98.079059	239.805687	81.734044	964.332268

Coefficient Interpretations:

• no_of_children: Holding all other features constant a 1 unit change in no_of_children will decrease the odds of a person canceling by 0.87 times or a 12.89% decrease in odds of canceling.
• no_of_weekend_nights: Holding all other features constant a 1 unit change in no_of_weekend_nights will decrease the odds of a person canceling by 0.95 times or a 4.82% decrease in odds of canceling.
• no_of_week_nights: Holding all other features constant a 1 unit change in no_of_week_nights will decrease the odds of a person canceling by 0.91 times or a 9.27% decrease in odds of canceling.
• required_car_parking_space: Holding all other features constant a 1 unit change in required_car_parking_space will increase the odds of a person canceling by 4.47 times or a 346.93% increase in odds of canceling.
• lead_time: Holding all other features constant a 1 unit change in lead_time will decrease the odds of a person canceling by 0.98 times or a 1.57% decrease in odds of canceling.
• repeated_guest: Holding all other features constant a 1 unit change in repeated_guest will increase the odds of a person canceling by 17.47 times or a 1646.86% increase in odds of canceling.
• avg_price_per_room: Holding all other features constant a 1 unit change in avg_price_per_room will decrease the odds of a person canceling by 0.98 times or a 1.63% decrease in odds of canceling.
• no_of_special_requests: Holding all other features constant a 1 unit change in no_of_special_requests will increase the odds of a person canceling by 4.39 times or a 339.21% increase in odds of canceling.
• Not Selected Meal Plan: Holding all other features constant a 1 unit change in Not Selected Meal Plan will decrease the odds of a person canceling by 0.72 times or a 27.91% decrease in odds of canceling.
• Room_Type 5: Holding all other features constant a 1 unit change in Room_Type 5 will increase the odds of a person canceling by 2.05 times or a 104.87% increase in odds of canceling.
• Room_Type 6: Holding all other features constant a 1 unit change in Room_Type 6 will increase the odds of a person canceling by 1.98 times or a 98.08% increase in odds of canceling.
• Room_Type 7: Holding all other features constant a 1 unit change in Room_Type 7 will increase the odds of a person canceling by 3.39 times or a 239.81% increase in odds of canceling.
• Corporate: Holding all other features constant a 1 unit change in Corporate will increase the odds of a person canceling by 1.82 times or a 81.73% increase in odds of canceling.
• Offline: Holding all other features constant a 1 unit change in Offline will increase the odds of a person canceling by 10.64 times or a 964.33% increase in odds of canceling.

# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

# creating confusion matrix
# Check Model on training set
confusion_matrix_statsmodels(lg11, X_train11, y_train)

log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg11, X_train11, y_train
)

print("Training performance:")
log_reg_model_train_perf

Training performance:

	Accuracy	Recall	Precision	F1
0	0.808913	0.909953	0.836618	0.871746

ROC-AUC on training set

logit_roc_auc_train = roc_auc_score(y_train, lg11.predict(X_train11))
fpr, tpr, thresholds = roc_curve(y_train, lg11.predict(X_train11))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

Logistic regression gives a good performance on the model.

# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg11.predict(X_train11))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.7203978395856949

# creating confusion matrix
confusion_matrix_statsmodels(
    lg11, X_train11, y_train, threshold=optimal_threshold_auc_roc
)

# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg11, X_train11, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:

	Accuracy	Recall	Precision	F1
0	0.772545	0.76725	0.89925	0.828022

Precision has increased from 0.837 to 0.899.

y_scores = lg11.predict(X_train11)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()

# setting the threshold
optimal_threshold_curve = 0.58

# creating confusion matrix
confusion_matrix_statsmodels(lg11, X_train11, y_train, threshold=optimal_threshold_curve)

log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg11, X_train11, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:

	Accuracy	Recall	Precision	F1
0	0.804402	0.869478	0.858295	0.86385

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.76 Threshold",
    "Logistic Regression-0.58 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.76 Threshold	Logistic Regression-0.58 Threshold
Accuracy	0.808913	0.772545	0.804402
Recall	0.909953	0.767250	0.869478
Precision	0.836618	0.899250	0.858295
F1	0.871746	0.828022	0.863850

X_test11 = X_test[list(X_train11.columns)]
# creating confusion matrix
confusion_matrix_statsmodels(lg11, X_test11, y_test)

log_reg_model_test_perf = model_performance_classification_statsmodels(
    lg11, X_test11, y_test
)

print("Test performance:")
log_reg_model_test_perf

Test performance:

	Accuracy	Recall	Precision	F1
0	0.810526	0.915273	0.835057	0.873326

logit_roc_auc_train = roc_auc_score(y_test, lg11.predict(X_test11))
fpr, tpr, thresholds = roc_curve(y_test, lg11.predict(X_test11))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg11, X_test11, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:

	Accuracy	Recall	Precision	F1
0	0.808858	0.873898	0.860432	0.867113

# creating confusion matrix
confusion_matrix_statsmodels(lg11, X_test11, y_test, threshold=optimal_threshold_auc_roc)

# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg11, X_test11, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:

	Accuracy	Recall	Precision	F1
0	0.775353	0.77352	0.897516	0.830918

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.76 Threshold",
    "Logistic Regression-0.58 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.76 Threshold	Logistic Regression-0.58 Threshold
Accuracy	0.808913	0.772545	0.804402
Recall	0.909953	0.767250	0.869478
Precision	0.836618	0.899250	0.858295
F1	0.871746	0.828022	0.863850

# testing performance comparison

models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.76 Threshold",
    "Logistic Regression-0.58 Threshold",
]

print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.76 Threshold	Logistic Regression-0.58 Threshold
Accuracy	0.810526	0.775353	0.808858
Recall	0.915273	0.773520	0.873898
Precision	0.835057	0.897516	0.860432
F1	0.873326	0.830918	0.867113

Conclusion

• We have been able to build a predictive model that can be used by the Hotels to find the bookings most likely to cancel with an f1_score of 0.87 and precision of .90 on the training set to identify bookings likely to be canceled.

• All the logistic regression models have given a generalized performance on the training and test set.

• Coefficient of some levels of required_car_parking_space, repeated_guest, Room Type 5, Room Type 6, Room Type 7, Corporate Customers, Offline bookings and no_of_special_requests are positive an increase in these will lead to increase in chances of a person not canceling their booking.

• Coefficient of number of children, number of weekend nights, number of week nights, lead time, avg_price_per_room, and No Selected Meal Plan are negative increase in these will lead to decrease in chances of a person not canceling their booking (aka canceling their booking).

Recommendations

• The hotel could offer coupons for repeated guests, since they have a lower chance of cancellation.
• Create advertisements specifically stating their parking availability to attract clients who require parking.
• Create a special marketing offer for Room Types 5, 6, and 7.
• Network with various corporations, possibly offering them special rates for their employees to stay at this hotel.
• Ensure you have a person available to do offline bookings to give the customer a seamless experience.

Building a Decision Tree model

import pandas as pd
import numpy as np
from sklearn import metrics
import matplotlib.pyplot as plt
%matplotlib inline 
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# Library to suppress warnings or deprecation notes
import warnings

warnings.filterwarnings("ignore")

# Libraries to help with reading and manipulating data

import pandas as pd
import numpy as np

# Library to split data
from sklearn.model_selection import train_test_split

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV

# To perform statistical analysis
import scipy.stats as stats

# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    plot_confusion_matrix,
    make_scorer,
)

# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

pdata.drop(['arrival_year_date'], axis = 1, inplace=True)
pdata.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25965 entries, 0 to 36273
Data columns (total 29 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          25965 non-null  int64  
 1   no_of_children                        25965 non-null  int64  
 2   no_of_weekend_nights                  25965 non-null  int64  
 3   no_of_week_nights                     25965 non-null  int64  
 4   required_car_parking_space            25965 non-null  int64  
 5   lead_time                             25965 non-null  int64  
 6   repeated_guest                        25965 non-null  int64  
 7   no_of_previous_cancellations          25965 non-null  int64  
 8   no_of_previous_bookings_not_canceled  25965 non-null  int64  
 9   avg_price_per_room                    25965 non-null  float64
 10  no_of_special_requests                25965 non-null  int64  
 11  date                                  25965 non-null  float64
 12  type_of_meal_plan_Meal Plan 1         25965 non-null  uint8  
 13  type_of_meal_plan_Meal Plan 2         25965 non-null  uint8  
 14  type_of_meal_plan_Meal Plan 3         25965 non-null  uint8  
 15  type_of_meal_plan_Not Selected        25965 non-null  uint8  
 16  room_type_reserved_Room_Type 1        25965 non-null  uint8  
 17  room_type_reserved_Room_Type 2        25965 non-null  uint8  
 18  room_type_reserved_Room_Type 3        25965 non-null  uint8  
 19  room_type_reserved_Room_Type 4        25965 non-null  uint8  
 20  room_type_reserved_Room_Type 5        25965 non-null  uint8  
 21  room_type_reserved_Room_Type 6        25965 non-null  uint8  
 22  room_type_reserved_Room_Type 7        25965 non-null  uint8  
 23  market_segment_type_Aviation          25965 non-null  uint8  
 24  market_segment_type_Complementary     25965 non-null  uint8  
 25  market_segment_type_Corporate         25965 non-null  uint8  
 26  market_segment_type_Offline           25965 non-null  uint8  
 27  market_segment_type_Online            25965 non-null  uint8  
 28  booking_status_Canceled               25965 non-null  uint8  
dtypes: float64(2), int64(10), uint8(17)
memory usage: 4.0 MB

X = pdata.drop("booking_status_Canceled" , axis=1)
y = pdata.pop("booking_status_Canceled")

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)

model = DecisionTreeClassifier(criterion="gini", random_state=1)
model.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

decision_tree_perf_train = model_performance_classification_sklearn(
    model, X_train, y_train
)
decision_tree_perf_train

	Accuracy	Recall	Precision	F1
0	0.996369	0.987438	1.0	0.993679

confusion_matrix_sklearn(model, X_train, y_train)

decision_tree_perf_test = model_performance_classification_sklearn(
    model, X_test, y_test
)
decision_tree_perf_test

	Accuracy	Recall	Precision	F1
0	0.818485	0.685465	0.672515	0.678928

feature_names = list(X.columns)
print(feature_names)

['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests', 'date', 'type_of_meal_plan_Meal Plan 1', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 1', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Aviation', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']

plt.figure(figsize=(20, 30))

out = tree.plot_tree(
    model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=True,
    class_names=True,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 4.50
|   |   |   |   |   |--- avg_price_per_room <= 201.50
|   |   |   |   |   |   |--- avg_price_per_room <= 78.10
|   |   |   |   |   |   |   |--- date <= 1505476815676768256.00
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1504958395944271872.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [37.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- date >  1504958395944271872.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- date >  1505476815676768256.00
|   |   |   |   |   |   |   |   |--- lead_time <= 17.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [31.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  17.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  78.10
|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |--- date <= 1503575966230773760.00
|   |   |   |   |   |   |   |   |   |--- date <= 1502668800418381824.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1502668800418381824.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- date >  1503575966230773760.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 86.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [58.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  86.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [326.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 61.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1502150380685885440.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1502150380685885440.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- lead_time >  61.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |--- avg_price_per_room >  201.50
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  4.50
|   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- avg_price_per_room <= 113.53
|   |   |   |   |   |--- date <= 1510660806843301888.00
|   |   |   |   |   |   |--- date <= 1505260761641910272.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 92.03
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  92.03
|   |   |   |   |   |   |   |   |--- lead_time <= 99.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  99.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 113.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  113.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1501848014988247040.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1501848014988247040.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- date >  1505260761641910272.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- date >  1510660806843301888.00
|   |   |   |   |   |   |--- date <= 1518393603401973760.00
|   |   |   |   |   |   |   |--- weights: [33.00, 0.00] class: 0
|   |   |   |   |   |   |--- date >  1518393603401973760.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 102.17
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.89
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.12
|   |   |   |   |   |   |   |   |   |   |--- date <= 1519732808564604928.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- date >  1519732808564604928.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.12
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.89
|   |   |   |   |   |   |   |   |   |--- lead_time <= 109.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.76
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.76
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  109.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 145.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  145.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- avg_price_per_room >  102.17
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  113.53
|   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 131.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- date <= 1528372839655145472.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1528372839655145472.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 108.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1537401548106104832.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1537401548106104832.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  108.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  131.00
|   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 9.50
|   |   |   |   |--- avg_price_per_room <= 202.67
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |--- date <= 1503403205466259456.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 133.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  133.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- date >  1503403205466259456.00
|   |   |   |   |   |   |   |   |   |--- date <= 1518048013153468416.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1504785635179757568.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- date >  1504785635179757568.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [137.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1518048013153468416.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1525996795027521536.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- date >  1525996795027521536.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [24.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 145.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  145.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 53.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  53.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- date <= 1517659198354096128.00
|   |   |   |   |   |   |   |--- date <= 1503835176097021952.00
|   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 102.94
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  102.94
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- date >  1503835176097021952.00
|   |   |   |   |   |   |   |   |--- date <= 1510531201910177792.00
|   |   |   |   |   |   |   |   |   |--- date <= 1510315216594796544.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1505433591125901312.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- date >  1505433591125901312.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [26.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1510315216594796544.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- date >  1510531201910177792.00
|   |   |   |   |   |   |   |   |   |--- weights: [62.00, 0.00] class: 0
|   |   |   |   |   |   |--- date >  1517659198354096128.00
|   |   |   |   |   |   |   |--- date <= 1537272011892457472.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1527897575754039296.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1527897575754039296.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 106.42
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  106.42
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |   |--- date >  1537272011892457472.00
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- date <= 1542715212925239296.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- date >  1542715212925239296.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 67.10
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  67.10
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  202.67
|   |   |   |   |   |--- date <= 1543881588603879424.00
|   |   |   |   |   |   |--- weights: [0.00, 21.00] class: 1
|   |   |   |   |   |--- date >  1543881588603879424.00
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- lead_time >  9.50
|   |   |   |   |--- date <= 1517399988487847936.00
|   |   |   |   |   |--- lead_time <= 76.50
|   |   |   |   |   |   |--- date <= 1499644799844614144.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |--- lead_time <= 57.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  57.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- date >  1499644799844614144.00
|   |   |   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 104.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [96.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  104.75
|   |   |   |   |   |   |   |   |   |   |--- date <= 1507852791584915456.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- date >  1507852791584915456.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |   |   |--- date <= 1514635197780328448.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 204.58
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 62.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  62.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  204.58
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- date >  1514635197780328448.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.15
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 44.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  44.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  76.50
|   |   |   |   |   |   |--- date <= 1501199990322626560.00
|   |   |   |   |   |   |   |--- weights: [0.00, 19.00] class: 1
|   |   |   |   |   |   |--- date >  1501199990322626560.00
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- date <= 1513080007302316032.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 79.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  79.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 84.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  84.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- date >  1513080007302316032.00
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |--- date >  1517399988487847936.00
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 104.97
|   |   |   |   |   |   |   |--- avg_price_per_room <= 56.57
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 54.08
|   |   |   |   |   |   |   |   |   |--- weights: [28.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  54.08
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 54.17
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 69.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  69.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  54.17
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  56.57
|   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |--- date <= 1543449617973116928.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- date >  1543449617973116928.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [41.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1528113629788897280.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- date >  1528113629788897280.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |--- avg_price_per_room >  104.97
|   |   |   |   |   |   |   |--- date <= 1540296012466225152.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 195.12
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 34
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 76.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  76.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  195.12
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 79.00] class: 1
|   |   |   |   |   |   |   |--- date >  1540296012466225152.00
|   |   |   |   |   |   |   |   |--- lead_time <= 46.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 146.90
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  146.90
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  46.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 56.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  56.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [56.00, 0.00] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- date <= 1501070385389502464.00
|   |   |   |--- avg_price_per_room <= 61.33
|   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |--- avg_price_per_room >  61.33
|   |   |   |   |--- avg_price_per_room <= 93.80
|   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |--- weights: [0.00, 31.00] class: 1
|   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |--- lead_time <= 84.00
|   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  84.00
|   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |--- avg_price_per_room >  93.80
|   |   |   |   |   |--- lead_time <= 91.00
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  91.00
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |--- date >  1501070385389502464.00
|   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.90
|   |   |   |   |   |   |   |   |--- weights: [257.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.90
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.15
|   |   |   |   |   |   |   |   |   |--- date <= 1518004857322078208.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1518004857322078208.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.15
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- date <= 1502971166116020224.00
|   |   |   |   |   |   |   |--- date <= 1502409590552133632.00
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- date >  1502409590552133632.00
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- date >  1502971166116020224.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 138.79
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.45
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1517745578736353280.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [74.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- date >  1517745578736353280.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  10.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.45
|   |   |   |   |   |   |   |   |   |--- date <= 1505044776326529024.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1505044776326529024.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- avg_price_per_room >  138.79
|   |   |   |   |   |   |   |   |--- date <= 1529798425200033792.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 192.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [24.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  192.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- date >  1529798425200033792.00
|   |   |   |   |   |   |   |   |   |--- date <= 1530662366461558784.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1530403156595310592.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- date >  1530403156595310592.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- date >  1530662366461558784.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 159.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  159.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 166.22
|   |   |   |   |   |   |   |   |   |--- lead_time <= 94.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [355.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  94.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  166.22
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |   |   |--- lead_time <= 108.00
|   |   |   |   |   |   |   |   |   |--- date <= 1530705659731902464.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- date >  1530705659731902464.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  108.00
|   |   |   |   |   |   |   |   |   |--- date <= 1522022404090494976.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [20.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1522022404090494976.00
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.95
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.95
|   |   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |--- date <= 1534679981949452288.00
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 137.25
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 128.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  128.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  137.25
|   |   |   |   |   |   |   |   |   |   |--- date <= 1532519991356686336.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- date >  1532519991356686336.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [131.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- date >  1534679981949452288.00
|   |   |   |   |   |   |   |   |--- date <= 1543579222906241024.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.69
|   |   |   |   |   |   |   |   |   |   |--- date <= 1538567992504221696.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- date >  1538567992504221696.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.69
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [21.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- date >  1543579222906241024.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [228.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 136.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  136.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |--- lead_time <= 89.50
|   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |--- weights: [1918.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.71
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.71
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1504051161412403200.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1504051161412403200.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |--- weights: [64.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- lead_time >  89.50
|   |   |   |   |   |--- avg_price_per_room <= 202.14
|   |   |   |   |   |   |--- date <= 1536494382293712896.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 6.50
|   |   |   |   |   |   |   |   |--- date <= 1516233612809207808.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 105.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  105.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 116.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  116.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- date >  1516233612809207808.00
|   |   |   |   |   |   |   |   |   |--- date <= 1535284782064205824.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1530791971394682880.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- date >  1530791971394682880.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- date >  1535284782064205824.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1535414386997329920.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- date >  1535414386997329920.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- no_of_week_nights >  6.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 130.68
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  130.68
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- date >  1536494382293712896.00
|   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 105.01
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  105.01
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |   |   |   |--- date <= 1545523228183625728.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- date >  1545523228183625728.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |--- weights: [34.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  202.14
|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- avg_price_per_room <= 89.76
|   |   |   |   |   |--- lead_time <= 402.00
|   |   |   |   |   |   |--- lead_time <= 274.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 27.07
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  27.07
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 171.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  171.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- lead_time >  274.00
|   |   |   |   |   |   |   |--- lead_time <= 318.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 293.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 277.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  277.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  293.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1530403156595310592.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- date >  1530403156595310592.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  318.50
|   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  402.00
|   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |--- avg_price_per_room >  89.76
|   |   |   |   |   |--- date <= 1528200010171154432.00
|   |   |   |   |   |   |--- date <= 1515844798009835520.00
|   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |--- date >  1515844798009835520.00
|   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |--- date >  1528200010171154432.00
|   |   |   |   |   |   |--- date <= 1537185562790723584.00
|   |   |   |   |   |   |   |--- lead_time <= 297.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 243.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.77
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.77
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  243.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  297.00
|   |   |   |   |   |   |   |   |--- lead_time <= 333.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  333.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- date >  1537185562790723584.00
|   |   |   |   |   |   |   |--- lead_time <= 291.00
|   |   |   |   |   |   |   |   |--- lead_time <= 224.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 156.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  156.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  224.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  291.00
|   |   |   |   |   |   |   |   |--- date <= 1539432002485223424.00
|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- date >  1539432002485223424.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |--- date <= 1506556810973151232.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- date >  1506556810973151232.00
|   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- date <= 1543751983670755328.00
|   |   |   |   |   |   |   |--- weights: [0.00, 340.00] class: 1
|   |   |   |   |   |   |--- date >  1543751983670755328.00
|   |   |   |   |   |   |   |--- date <= 1543967968986136576.00
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- date >  1543967968986136576.00
|   |   |   |   |   |   |   |   |--- lead_time <= 215.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 212.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  212.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  215.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 43.00] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- date <= 1541678442179723264.00
|   |   |   |   |   |   |--- avg_price_per_room <= 99.83
|   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |--- weights: [39.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |--- date <= 1533211240573173760.00
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- date >  1533211240573173760.00
|   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  99.83
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- date >  1541678442179723264.00
|   |   |   |   |   |   |--- avg_price_per_room <= 82.10
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  82.10
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 356.00
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  356.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |--- date <= 1544097573919260672.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 89.00] class: 1
|   |   |   |   |   |   |   |--- date >  1544097573919260672.00
|   |   |   |   |   |   |   |   |--- lead_time <= 301.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.87
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.87
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  301.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- date <= 1544788823135748096.00
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- avg_price_per_room <= 94.21
|   |   |   |   |   |   |   |--- date <= 1540641602714730496.00
|   |   |   |   |   |   |   |   |--- lead_time <= 372.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 165.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  165.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- lead_time >  372.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- date >  1540641602714730496.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.15
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.15
|   |   |   |   |   |   |   |   |   |--- date <= 1540727983096987648.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- date >  1540727983096987648.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- avg_price_per_room >  94.21
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 <= 0.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- date <= 1531439996060303360.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- date >  1531439996060303360.00
|   |   |   |   |   |   |   |   |   |   |--- date <= 1533816040687927296.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- date >  1533816040687927296.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 1 >  0.50
|   |   |   |   |   |   |   |   |--- date <= 1540166407533101056.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1538092797322592256.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- date >  1538092797322592256.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- date <= 1534723206500319232.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- date >  1534723206500319232.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- date >  1540166407533101056.00
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |--- date >  1544788823135748096.00
|   |   |   |   |   |--- date <= 1546084803747512320.00
|   |   |   |   |   |   |--- lead_time <= 281.50
|   |   |   |   |   |   |   |--- date <= 1545912042982998016.00
|   |   |   |   |   |   |   |   |--- lead_time <= 252.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.81
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 245.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  245.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.81
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  252.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |--- date >  1545912042982998016.00
|   |   |   |   |   |   |   |   |--- no_of_children <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_children >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- lead_time >  281.50
|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- date >  1546084803747512320.00
|   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- no_of_special_requests <= 2.50
|   |   |   |--- date <= 1543492773804507136.00
|   |   |   |   |--- date <= 1518220773917982720.00
|   |   |   |   |   |--- date <= 1511395211891179520.00
|   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1
|   |   |   |   |   |--- date >  1511395211891179520.00
|   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- date >  1518220773917982720.00
|   |   |   |   |   |--- weights: [0.00, 962.00] class: 1
|   |   |   |--- date >  1543492773804507136.00
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- weights: [35.00, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 <= 0.50
|   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 110.74
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.00
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  110.74
|   |   |   |   |   |   |   |   |--- lead_time <= 162.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  162.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |--- room_type_reserved_Room_Type 1 >  0.50
|   |   |   |   |   |   |--- lead_time <= 151.50
|   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- lead_time >  151.50
|   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |--- no_of_special_requests >  2.50
|   |   |   |--- weights: [32.00, 0.00] class: 0

importances = model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

Do we need to prune the tree?

## Function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth  
    
    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    
##  Function to calculate recall score
def get_recall_score(model):
    '''
    model : classifier to predict values of X

    '''
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)
    print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
    print("Recall on test set : ",metrics.recall_score(y_test,pred_test))

# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from

parameters = {
    "max_depth": [np.arange(2, 50, 5), None],
    "criterion": ["entropy", "gini"],
    "splitter": ["best", "random"],
    "min_impurity_decrease": [0.000001, 0.00001, 0.0001],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train11, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(min_impurity_decrease=1e-05, random_state=1)

decision_tree_tune_perf_train = model_performance_classification_sklearn(
    estimator, X_train, y_train
)
decision_tree_tune_perf_train

	Accuracy	Recall	Precision	F1
0	0.993783	0.978683	0.999806	0.989131

confusion_matrix_sklearn(estimator, X_train, y_train)

decision_tree_tune_perf_test = model_performance_classification_sklearn(
    estimator, X_test, y_test
)

decision_tree_tune_perf_test

	Accuracy	Recall	Precision	F1
0	0.821566	0.681797	0.681173	0.681485

confusion_matrix_sklearn(estimator, X_test, y_test)

# Accuracy on train and test
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))
# Recall on train and test
get_recall_score(dTree1)

dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

make_confusion_matrix(dTree1, y_test)

# Accuracy on train and test
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))
# Recall on train and test
get_recall_score(dTree1)

Accuracy on training set :  0.78585969738652
Accuracy on test set :  0.7880616174582799
Recall on training set :  0.724971450323563
Recall on test set :  0.7235213204951857

plt.figure(figsize=(15,10))

tree.plot_tree(dTree1,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(dTree1,feature_names=feature_names,show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [2504.00, 245.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2202.00, 2313.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- date <= 1501070385389502464.00
|   |   |   |--- weights: [5.00, 43.00] class: 1
|   |   |--- date >  1501070385389502464.00
|   |   |   |--- weights: [7512.00, 1003.00] class: 0
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- weights: [193.00, 456.00] class: 1
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- weights: [426.00, 197.00] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- no_of_special_requests <= 2.50
|   |   |   |--- weights: [47.00, 997.00] class: 1
|   |   |--- no_of_special_requests >  2.50
|   |   |   |--- weights: [32.00, 0.00] class: 0

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(dTree1.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                           Imp
lead_time                             0.391170
market_segment_type_Online            0.259427
no_of_special_requests                0.240515
avg_price_per_room                    0.084389
date                                  0.024498
room_type_reserved_Room_Type 1        0.000000
market_segment_type_Offline           0.000000
market_segment_type_Corporate         0.000000
market_segment_type_Complementary     0.000000
market_segment_type_Aviation          0.000000
room_type_reserved_Room_Type 7        0.000000
room_type_reserved_Room_Type 6        0.000000
room_type_reserved_Room_Type 5        0.000000
room_type_reserved_Room_Type 4        0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 2        0.000000
no_of_adults                          0.000000
type_of_meal_plan_Not Selected        0.000000
no_of_children                        0.000000
type_of_meal_plan_Meal Plan 2         0.000000
type_of_meal_plan_Meal Plan 1         0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
repeated_guest                        0.000000
required_car_parking_space            0.000000
no_of_week_nights                     0.000000
no_of_weekend_nights                  0.000000
type_of_meal_plan_Meal Plan 3         0.000000

importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• You can see in important features of previous model, lead time was also was on top, but here importance of amount variable is zero this is the shortcoming of pre pruning, we just limit it even before knowing the importance of features and split.

• This is a good first pass fit, but we will continue with GridSearch to give a full comparison for the model.

from sklearn.model_selection import GridSearchCV

# Choose the type of classifier. 
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
## add from article
parameters = {'max_depth': np.arange(1,10), 
              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
              'max_leaf_nodes' : [2, 3, 5, 10],
              'min_impurity_decrease': [0.001,0.01,0.1]
             }

# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
estimator.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)

make_confusion_matrix(estimator,y_test)

plt.figure(figsize=(15,10))

tree.plot_tree(estimator,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(estimator,feature_names=feature_names,show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [2504.00, 245.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2202.00, 2313.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- weights: [7517.00, 1046.00] class: 0
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- weights: [619.00, 653.00] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- weights: [79.00, 997.00] class: 1

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(estimator.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

#Here we will see that importance of features has increased

                                           Imp
lead_time                             0.429267
market_segment_type_Online            0.284693
no_of_special_requests                0.193433
avg_price_per_room                    0.092608
room_type_reserved_Room_Type 1        0.000000
market_segment_type_Offline           0.000000
market_segment_type_Corporate         0.000000
market_segment_type_Complementary     0.000000
market_segment_type_Aviation          0.000000
room_type_reserved_Room_Type 7        0.000000
room_type_reserved_Room_Type 6        0.000000
room_type_reserved_Room_Type 5        0.000000
room_type_reserved_Room_Type 4        0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 2        0.000000
no_of_adults                          0.000000
type_of_meal_plan_Not Selected        0.000000
no_of_children                        0.000000
type_of_meal_plan_Meal Plan 2         0.000000
type_of_meal_plan_Meal Plan 1         0.000000
date                                  0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
repeated_guest                        0.000000
required_car_parking_space            0.000000
no_of_week_nights                     0.000000
no_of_weekend_nights                  0.000000
type_of_meal_plan_Meal Plan 3         0.000000

importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

Pre Pruning tested above, now Post Pruning tested below.

clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities

pd.DataFrame(path)

	ccp_alphas	impurities
0	0.000000	0.003631
1	0.000000	0.003631
2	0.000000	0.003631
3	0.000000	0.003631
4	0.000000	0.003631
...	...	...
1108	0.006623	0.277423
1109	0.010952	0.288375
1110	0.015335	0.303710
1111	0.028273	0.360256
1112	0.050768	0.411024

1113 rows × 2 columns

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(X_train, y_train)
    clfs.append(clf)
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
      clfs[-1].tree_.node_count, ccp_alphas[-1]))

Number of nodes in the last tree is: 1 with ccp_alpha: 0.050767983648866255

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1,figsize=(10,7))
ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]

fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()

index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print('Training accuracy of best model: ',best_model.score(X_train, y_train))
print('Test accuracy of best model: ',best_model.score(X_test, y_test))

DecisionTreeClassifier(ccp_alpha=0.0002428734316759681, random_state=1)
Training accuracy of best model:  0.8652544704264099
Test accuracy of best model:  0.8581514762516046

dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

Since accuracy isn't the right metric for our data we would want high recall

recall_train=[]
for clf in clfs:
    pred_train3=clf.predict(X_train)
    values_train=metrics.recall_score(y_train,pred_train3)
    recall_train.append(values_train)

recall_test=[]
for clf in clfs:
    pred_test3=clf.predict(X_test)
    values_test=metrics.recall_score(y_test,pred_test3)
    recall_test.append(values_test)

fig, ax = plt.subplots(figsize=(15,5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()

# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.015334740776881739, random_state=1)

make_confusion_matrix(best_model,y_test)

# Recall on train and test
get_recall_score(best_model)

Recall on training set :  0.7542824514655501
Recall on test set :  0.7524071526822559

plt.figure(figsize=(17,15))

tree.plot_tree(best_model,feature_names=feature_names,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(best_model,feature_names=feature_names,show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [2504.00, 245.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2202.00, 2313.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- weights: [7517.00, 1046.00] class: 0
|--- lead_time >  150.50
|   |--- weights: [698.00, 1650.00] class: 1

# importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print (pd.DataFrame(best_model.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                           Imp
lead_time                             0.473077
market_segment_type_Online            0.313749
no_of_special_requests                0.213174
type_of_meal_plan_Not Selected        0.000000
market_segment_type_Offline           0.000000
market_segment_type_Corporate         0.000000
market_segment_type_Complementary     0.000000
market_segment_type_Aviation          0.000000
room_type_reserved_Room_Type 7        0.000000
room_type_reserved_Room_Type 6        0.000000
room_type_reserved_Room_Type 5        0.000000
room_type_reserved_Room_Type 4        0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 2        0.000000
room_type_reserved_Room_Type 1        0.000000
no_of_adults                          0.000000
no_of_children                        0.000000
type_of_meal_plan_Meal Plan 2         0.000000
type_of_meal_plan_Meal Plan 1         0.000000
date                                  0.000000
avg_price_per_room                    0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
repeated_guest                        0.000000
required_car_parking_space            0.000000
no_of_week_nights                     0.000000
no_of_weekend_nights                  0.000000
type_of_meal_plan_Meal Plan 3         0.000000

importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

Model Performance Comparison and Conclusions

comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[1,0.53,0.51,0.63], 'Test_Recall':[0.46,0.46,0.46,0.56]}) 
comparison_frame

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	1.00	0.46
1	Decision tree with restricted maximum depth	0.53	0.46
2	Decision treee with hyperparameter tuning	0.51	0.46
3	Decision tree with post-pruning	0.63	0.56

Since we will prioritize Test Recall, the best tree for this dataset would be the decision tree with post-pruning.

Actionable Insights and Recommendations

• What profitable policies for cancellations and refunds can the hotel adopt?
• What other recommedations would you suggest to the hotel?

The three parameters of most importance when canceling a booking are lead time, Online Market Segment, and number of special requests.

• Lead time is important, but as long as the cancellation is early enough, the room can be re-booked. Perhaps impose a penalty for canceling the room after a certain date which would make re-booking difficult.

• Online booking services are convenient, but perhaps additional requirements should be added for these bookings. A similar penalty for late cancellation could apply to this also in the terms and conditions.

• Perhaps keep a system to track number of special requests. Then the booking could be flagged as unreliable or charge additional fees for certain requests.